From e441711741a3be2b9b0e2c59a827f82f8ca7604f Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Fri, 1 Nov 2024 14:31:46 +0100 Subject: [PATCH 01/13] Add files via upload Added doc about carla traffic rules. --- .../paf24/general/carla_traffic_rules.md | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 doc/research/paf24/general/carla_traffic_rules.md diff --git a/doc/research/paf24/general/carla_traffic_rules.md b/doc/research/paf24/general/carla_traffic_rules.md new file mode 100644 index 00000000..5e0acef2 --- /dev/null +++ b/doc/research/paf24/general/carla_traffic_rules.md @@ -0,0 +1,40 @@ +# Traffic Rules in CARLA + +## Introduction +CARLA (Car Learning to Act) is an open-source simulator designed for the development, training, and validation of autonomous driving systems. While there isn't a specific set of traffic laws from any real-world jurisdiction strictly implemented in CARLA, the simulation emphasizes key traffic behaviors and principles essential for creating a realistic driving environment, particularly in urban settings. + +## Key Traffic Principles Emphasized in CARLA +- **Traffic Signals**: + - Vehicles must respond to traffic lights, stopping at red lights and proceeding on green. + +- **Stop Signs**: + - Cars are required to stop at stop signs and yield to other vehicles and pedestrians. + +- **Lane Management**: + - Vehicles should remain within their designated lanes, respecting lane markings. + +- **Speed Limits**: + - Various areas within the simulation have established speed limits that vehicles must adhere to. + +- **Pedestrian Rights**: + - Vehicles must yield to pedestrians at designated crossings. + +These principles are vital for developing and validating algorithms for autonomous vehicles, as they mimic the rules that drivers are expected to follow in real-world urban traffic scenarios. + +## Focus on Urban Environments +CARLA is particularly designed to simulate urban scenarios, which are characterized by complex interactions among various traffic participants, including vehicles, pedestrians, and cyclists. The focus on urban environments makes it essential for testing systems under conditions that resemble real city driving dynamics. + +## Road Network Creation with ASAM OpenDRIVE® +Road networks in CARLA are created using the **ASAM OpenDRIVE®** format, which provides a standardized method for describing road infrastructures. This standardization allows for: +- **Hierarchical Structure**: Road networks are organized in nodes, facilitating specialized applications while ensuring interoperability. +- **Reference Line**: The core of every road network is the reference line, which anchors the geometry of roads and lanes. +- **Interconnectivity**: Roads can be interconnected, enabling realistic traffic behavior and routing. + +The integration of ASAM OpenDRIVE ensures that road networks can be efficiently modeled and shared across different simulation platforms, enhancing the realism of the driving scenarios. + +## Flexibility and Customization +CARLA provides the flexibility for users to define or modify traffic rules based on specific research requirements, enabling a wide range of testing scenarios. This adaptability is crucial for the development and validation of autonomous driving technologies. + +## References +- CARLA Documentation: [CARLA](https://carla.readthedocs.io/en/latest/) +- ASAM OpenDRIVE® Standard: [ASAM OpenDRIVE](https://www.asam.net/standards/detail/opendrive/) From 9b74b1f4c6aa5a111c4dbcaaa0c3a26daa50d167 Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Fri, 1 Nov 2024 15:02:51 +0100 Subject: [PATCH 02/13] Updated carla_traffic_rules.md --- .../paf24/general/carla_traffic_rules.md | 51 +++++++++---------- 1 file changed, 25 insertions(+), 26 deletions(-) diff --git a/doc/research/paf24/general/carla_traffic_rules.md b/doc/research/paf24/general/carla_traffic_rules.md index 5e0acef2..0793bd87 100644 --- a/doc/research/paf24/general/carla_traffic_rules.md +++ b/doc/research/paf24/general/carla_traffic_rules.md @@ -1,39 +1,38 @@ # Traffic Rules in CARLA ## Introduction -CARLA (Car Learning to Act) is an open-source simulator designed for the development, training, and validation of autonomous driving systems. While there isn't a specific set of traffic laws from any real-world jurisdiction strictly implemented in CARLA, the simulation emphasizes key traffic behaviors and principles essential for creating a realistic driving environment, particularly in urban settings. - -## Key Traffic Principles Emphasized in CARLA -- **Traffic Signals**: - - Vehicles must respond to traffic lights, stopping at red lights and proceeding on green. - -- **Stop Signs**: - - Cars are required to stop at stop signs and yield to other vehicles and pedestrians. - -- **Lane Management**: - - Vehicles should remain within their designated lanes, respecting lane markings. - -- **Speed Limits**: - - Various areas within the simulation have established speed limits that vehicles must adhere to. - -- **Pedestrian Rights**: - - Vehicles must yield to pedestrians at designated crossings. - -These principles are vital for developing and validating algorithms for autonomous vehicles, as they mimic the rules that drivers are expected to follow in real-world urban traffic scenarios. +CARLA (Car Learning to Act) is an open-source simulator for developing and testing autonomous driving systems. There is no strict set of traffic laws implemented, but key traffic behaviors are emphasized to create a realistic urban driving environment. + +## Key Traffic Principles in CARLA +- **Traffic Signals**: Vehicles must obey traffic lights—stop at red and go at green. +- **Stop Signs**: Cars must stop at stop signs and yield to pedestrians and other vehicles. +- **Lane Management**: Vehicles should stay in their lanes. +- **Speed Limits**: There are speed limits in different areas of the simulation. +- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks. + +These principles help in developing algorithms for autonomous vehicles by simulating real-world traffic rules. ## Focus on Urban Environments -CARLA is particularly designed to simulate urban scenarios, which are characterized by complex interactions among various traffic participants, including vehicles, pedestrians, and cyclists. The focus on urban environments makes it essential for testing systems under conditions that resemble real city driving dynamics. +CARLA focuses on urban scenarios with interactions among vehicles, pedestrians, and cyclists. Examples include: +- **Intersections**: Handling traffic lights and stop signs. +- **Roundabouts**: Yielding to cars already in the circle. +- **Crosswalks**: Stopping for pedestrians. +- **Bike Lanes**: Watching out for cyclists. +- **Parking**: Maneuvering in tight spaces. + +These scenarios are essential for training self-driving systems in complex environments. ## Road Network Creation with ASAM OpenDRIVE® -Road networks in CARLA are created using the **ASAM OpenDRIVE®** format, which provides a standardized method for describing road infrastructures. This standardization allows for: -- **Hierarchical Structure**: Road networks are organized in nodes, facilitating specialized applications while ensuring interoperability. -- **Reference Line**: The core of every road network is the reference line, which anchors the geometry of roads and lanes. -- **Interconnectivity**: Roads can be interconnected, enabling realistic traffic behavior and routing. +Road networks in CARLA are created using **ASAM OpenDRIVE®**, which standardizes road descriptions: +- **Hierarchical Structure**: Roads are organized in nodes for better application integration. +- **Reference Line**: Each road has a reference line to define its shape. +- **Interconnectivity**: Roads can connect for realistic traffic flow. -The integration of ASAM OpenDRIVE ensures that road networks can be efficiently modeled and shared across different simulation platforms, enhancing the realism of the driving scenarios. +## Units of Measurement +In CARLA, speed is measured in meters per second (m/s), and distances are in meters, ensuring consistency in simulations. ## Flexibility and Customization -CARLA provides the flexibility for users to define or modify traffic rules based on specific research requirements, enabling a wide range of testing scenarios. This adaptability is crucial for the development and validation of autonomous driving technologies. +CARLA allows users to modify traffic rules and create custom maps. This flexibility enables researchers to design specific scenarios, enhancing the testing of self-driving technologies. ## References - CARLA Documentation: [CARLA](https://carla.readthedocs.io/en/latest/) From 5b10bff6ed1d88447bfe78136bcc20c9ee6cdf23 Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 10:46:40 +0100 Subject: [PATCH 03/13] Fixed Linter markdown and added some information to the doc. --- .../paf24/general/carla_traffic_rules.md | 95 +++++++++++++++---- 1 file changed, 75 insertions(+), 20 deletions(-) diff --git a/doc/research/paf24/general/carla_traffic_rules.md b/doc/research/paf24/general/carla_traffic_rules.md index 0793bd87..7e8fbb2e 100644 --- a/doc/research/paf24/general/carla_traffic_rules.md +++ b/doc/research/paf24/general/carla_traffic_rules.md @@ -1,39 +1,94 @@ -# Traffic Rules in CARLA +# Traffic Rules and Principles in CARLA + +## Table of Contents + +- [Introduction](#introduction) +- [Key Traffic Principles and Complex Interactions](#key-traffic-principles-and-complex-interactions) +- [CARLA Maps and Their Traffic Scenarios](#carla-maps-and-their-traffic-scenarios) + - [Overview of CARLA Maps](#overview-of-carla-maps) + - [Rural and Highway Scenarios](#rural-and-highway-scenarios) + - [Custom Maps with ASAM OpenDRIVE®](#custom-maps-with-asam-opendrive) +- [Units of Measurement](#units-of-measurement) +- [References](#references) ## Introduction -CARLA (Car Learning to Act) is an open-source simulator for developing and testing autonomous driving systems. There is no strict set of traffic laws implemented, but key traffic behaviors are emphasized to create a realistic urban driving environment. -## Key Traffic Principles in CARLA -- **Traffic Signals**: Vehicles must obey traffic lights—stop at red and go at green. -- **Stop Signs**: Cars must stop at stop signs and yield to pedestrians and other vehicles. -- **Lane Management**: Vehicles should stay in their lanes. -- **Speed Limits**: There are speed limits in different areas of the simulation. -- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks. +CARLA (Car Learning to Act) is an open-source simulator specifically designed for testing and developing autonomous driving systems. It incorporates essential traffic rules to create a flexible environment. This flexibility allows users to modify traffic regulations and design custom maps, enabling the evaluation of driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. + +## Key Traffic Principles and Complex Interactions + +- **Traffic Signals**: Vehicles must stop at red lights and proceed on green, facilitating the testing of traffic light detection and timing adjustments. + +- **Stop Signs**: Vehicles are required to halt at stop signs, yielding to pedestrians and other vehicles, which is essential for ensuring safety in traffic scenarios. + +- **Lane Management**: Cars are expected to stay within designated lanes, aiding the development of lane-keeping and lane-following algorithms. + +- **Speed Limits**: Varying speed limits across different map zones allow for the testing of speed regulation and adherence to traffic laws. + +- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, which is crucial for pedestrian detection and ensuring safe braking. + +- **Intersection Management**: Vehicles must effectively navigate intersections, managing stop lights and stop signs while yielding as necessary to maintain traffic flow. + +- **Roundabout Navigation**: Vehicles are required to yield to cars already within the roundabout, supporting testing of circular navigation and merging strategies. + +- **Crosswalk Behavior**: Vehicles must detect and stop for pedestrians at crosswalks, highlighting the importance of pedestrian safety. + +- **Bicycle Lane Navigation**: Vehicles should be able to detect and navigate around cyclists, ensuring safe interactions in mixed traffic environments. + +- **Parking Maneuvers**: Vehicles are tested on their ability to maneuver in tight parking spaces, simulating real-life parking constraints. + +## CARLA Maps and Their Traffic Scenarios + +CARLA features 12 maps, each designed to test various aspects of autonomous driving, ranging from basic navigation to complex urban and rural settings. + +### Overview of CARLA Maps + +- **Town 1**: A small town with T-junctions and bridges, focusing on intersection management and lane discipline. -These principles help in developing algorithms for autonomous vehicles by simulating real-world traffic rules. +- **Town 2**: Similar to Town 1, it includes commercial and residential areas, testing navigation and environmental awareness. -## Focus on Urban Environments -CARLA focuses on urban scenarios with interactions among vehicles, pedestrians, and cyclists. Examples include: -- **Intersections**: Handling traffic lights and stop signs. -- **Roundabouts**: Yielding to cars already in the circle. -- **Crosswalks**: Stopping for pedestrians. -- **Bike Lanes**: Watching out for cyclists. -- **Parking**: Maneuvering in tight spaces. +- **Town 3**: A larger urban area with roundabouts and underpasses, assessing roundabout navigation and adaptive speed control. -These scenarios are essential for training self-driving systems in complex environments. +- **Town 4**: A small town with a figure-eight ring road, testing multi-lane navigation and pedestrian interactions. + +- **Town 5**: An urban environment featuring multilane roads and a highway, focusing on highway navigation and commercial driving strategies. + +- **Town 6**: A low-density town with unique junctions, testing multi-lane navigation and slip road utilization. + +- **Town 7**: A rural community with unmarked roads, challenging navigation and parking in residential areas. + +- **Town 8 & Town 9**: Secret maps used for the Leaderboard challenge. + +- **Town 10**: An inner-city area with diverse junctions and pedestrian activity, testing negotiation skills in complex traffic. + +- **Town 11**: A minimally decorated map for testing navigation over expansive areas with fewer visual cues. + +- **Town 12**: A large map inspired by Amarillo, Texas, with urban, residential, and rural areas, testing navigation and visual perception across diverse environments. + +### Rural and Highway Scenarios + +Maps like **Town 7** and **Town 12** emphasize rural and highway scenarios, simulating open and high-speed environments. They test long-distance driving, speed regulation, and safe overtaking, requiring adaptability to unmarked roads and agricultural structures in rural settings. ## Road Network Creation with ASAM OpenDRIVE® + Road networks in CARLA are created using **ASAM OpenDRIVE®**, which standardizes road descriptions: + - **Hierarchical Structure**: Roads are organized in nodes for better application integration. + - **Reference Line**: Each road has a reference line to define its shape. + - **Interconnectivity**: Roads can connect for realistic traffic flow. ## Units of Measurement -In CARLA, speed is measured in meters per second (m/s), and distances are in meters, ensuring consistency in simulations. -## Flexibility and Customization -CARLA allows users to modify traffic rules and create custom maps. This flexibility enables researchers to design specific scenarios, enhancing the testing of self-driving technologies. +To ensure consistency across tests, CARLA employs standardized units: + +- **Speed**: Measured in meters per second (m/s). + +- **Distance**: Measured in meters. ## References + - CARLA Documentation: [CARLA](https://carla.readthedocs.io/en/latest/) + - ASAM OpenDRIVE® Standard: [ASAM OpenDRIVE](https://www.asam.net/standards/detail/opendrive/) From 9ddb9d7e5447f4887d1573a96abae1a895e3f574 Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 10:56:38 +0100 Subject: [PATCH 04/13] Next Markdown Linter fix. --- carla_traffic_rules.md | 94 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 carla_traffic_rules.md diff --git a/carla_traffic_rules.md b/carla_traffic_rules.md new file mode 100644 index 00000000..e705e258 --- /dev/null +++ b/carla_traffic_rules.md @@ -0,0 +1,94 @@ +# Traffic Rules and Principles in CARLA + +## Table of Contents + +- [Introduction](#introduction) +- [Key Traffic Principles and Complex Interactions](#key-traffic-principles-and-complex-interactions) +- [CARLA Maps and Their Traffic Scenarios](#carla-maps-and-their-traffic-scenarios) + - [Overview of CARLA Maps](#overview-of-carla-maps) + - [Rural and Highway Scenarios](#rural-and-highway-scenarios) + - [Custom Maps with ASAM OpenDRIVE®](#custom-maps-with-asam-opendrive) +- [Units of Measurement](#units-of-measurement) +- [References](#references) + +## Introduction + +CARLA (Car Learning to Act) is an open-source simulator designed for testing and developing self-driving car systems. It incorporates essential traffic rules to create a flexible environment. Users can modify traffic regulations and create custom maps, helping them test driving behaviors and interactions without strictly following the traffic laws of any specific country. + +## Key Traffic Principles and Complex Interactions + +- **Traffic Signals**: Vehicles must stop at red lights and go on green. + +- **Stop Signs**: Vehicles must stop at stop signs and yield to pedestrians and other cars. + +- **Lane Management**: Cars should remain in their designated lanes. + +- **Speed Limits**: Different speed limits in various areas of the map allow testing of speed control and adherence to traffic laws. + +- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, which is vital for pedestrian detection and ensuring safe stops. + +- **Intersection Management**: Vehicles must navigate intersections effectively, managing stop lights and stop signs while yielding when necessary to keep traffic moving. + +- **Roundabout Navigation**: Vehicles should yield to cars already in the roundabout. This tests their ability to navigate circular roads and merge correctly. + +- **Crosswalk Behavior**: Vehicles need to detect and stop for pedestrians at crosswalks, highlighting the importance of pedestrian safety. + +- **Bicycle Lane Navigation**: Vehicles should be able to detect and safely navigate around cyclists in traffic. + +- **Parking Maneuvers**: Vehicles are tested on their ability to park in tight spaces, simulating real-life parking challenges. + +## CARLA Maps and Their Traffic Scenarios + +CARLA has 12 maps, each designed to test different aspects of self-driving technology, from basic navigation to complex urban and rural situations. + +### Overview of CARLA Maps + +- **Town 1**: A small town with T-junctions and bridges, focusing on managing intersections and staying in lanes. + +- **Town 2**: Similar to Town 1, this town includes shops and homes, testing navigation and environmental awareness. + +- **Town 3**: A larger city area with roundabouts and underpasses, assessing navigation in roundabouts and speed adjustments. + +- **Town 4**: A small town with a figure-eight road, testing navigation in multiple lanes and interactions with pedestrians. + +- **Town 5**: An urban area with multilane roads and a highway, focusing on highway driving and commercial strategies. + +- **Town 6**: A less populated town with unique junctions, testing navigation in multiple lanes and the use of slip roads. + +- **Town 7**: A rural community with no marked roads, which tests navigation and parking in residential areas. + +- **Town 8 & Town 9**: Hidden maps used for the Leaderboard challenge. + +- **Town 10**: An inner-city area with various junctions and pedestrian activity, testing skills in complex traffic situations. + +- **Town 11**: A basic map for testing navigation in open areas with fewer visual guides. + +- **Town 12**: A large map based on Amarillo, Texas, featuring urban, residential, and rural areas to test navigation and visual skills across diverse environments. + +### Rural and Highway Scenarios + +Maps like **Town 7** and **Town 12** focus on rural and highway driving, simulating open and high-speed areas. They test long-distance driving, speed management, and safe overtaking, requiring drivers to adapt to unmarked roads and agricultural settings. + +## Road Network Creation with ASAM OpenDRIVE® + +Road networks in CARLA are built using **ASAM OpenDRIVE®**, which standardizes road descriptions: + +- **Hierarchical Structure**: Roads are organized into nodes for better integration. + +- **Reference Line**: Each road has a reference line that defines its shape. + +- **Interconnectivity**: Roads can connect to each other for realistic traffic flow. + +## Units of Measurement + +To keep tests consistent, CARLA uses standardized units: + +- **Speed**: Measured in meters per second (m/s). + +- **Distance**: Measured in meters. + +## References + +- CARLA Documentation: [CARLA](https://carla.readthedocs.io/en/latest/) + +- ASAM OpenDRIVE® Standard: [ASAM OpenDRIVE](https://www.asam.net/standards/detail/opendrive/) From 4a00b94c49dbba2a4275616b04cb0ba3466c90bd Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 10:59:44 +0100 Subject: [PATCH 05/13] Next next linter fix. --- carla_traffic_rules.md | 80 +++++++++++++++++++++++++++++------------- 1 file changed, 56 insertions(+), 24 deletions(-) diff --git a/carla_traffic_rules.md b/carla_traffic_rules.md index e705e258..c34c2bc1 100644 --- a/carla_traffic_rules.md +++ b/carla_traffic_rules.md @@ -13,71 +13,103 @@ ## Introduction -CARLA (Car Learning to Act) is an open-source simulator designed for testing and developing self-driving car systems. It incorporates essential traffic rules to create a flexible environment. Users can modify traffic regulations and create custom maps, helping them test driving behaviors and interactions without strictly following the traffic laws of any specific country. +CARLA (Car Learning to Act) is an open-source simulator designed for testing +and developing self-driving car systems. It incorporates essential traffic +rules to create a flexible environment. Users can modify traffic regulations +and create custom maps, helping them test driving behaviors and interactions +without strictly following the traffic laws of any specific country. ## Key Traffic Principles and Complex Interactions - **Traffic Signals**: Vehicles must stop at red lights and go on green. -- **Stop Signs**: Vehicles must stop at stop signs and yield to pedestrians and other cars. +- **Stop Signs**: Vehicles must stop at stop signs and yield to pedestrians + and other cars. - **Lane Management**: Cars should remain in their designated lanes. -- **Speed Limits**: Different speed limits in various areas of the map allow testing of speed control and adherence to traffic laws. +- **Speed Limits**: Different speed limits in various areas of the map allow + testing of speed control and adherence to traffic laws. -- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, which is vital for pedestrian detection and ensuring safe stops. +- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, + which is vital for pedestrian detection and ensuring safe stops. -- **Intersection Management**: Vehicles must navigate intersections effectively, managing stop lights and stop signs while yielding when necessary to keep traffic moving. +- **Intersection Management**: Vehicles must navigate intersections + effectively, managing stop lights and stop signs while yielding when + necessary to keep traffic moving. -- **Roundabout Navigation**: Vehicles should yield to cars already in the roundabout. This tests their ability to navigate circular roads and merge correctly. +- **Roundabout Navigation**: Vehicles should yield to cars already in the + roundabout. This tests their ability to navigate circular roads and merge + correctly. -- **Crosswalk Behavior**: Vehicles need to detect and stop for pedestrians at crosswalks, highlighting the importance of pedestrian safety. +- **Crosswalk Behavior**: Vehicles need to detect and stop for pedestrians + at crosswalks, highlighting the importance of pedestrian safety. -- **Bicycle Lane Navigation**: Vehicles should be able to detect and safely navigate around cyclists in traffic. +- **Bicycle Lane Navigation**: Vehicles should be able to detect and safely + navigate around cyclists in traffic. -- **Parking Maneuvers**: Vehicles are tested on their ability to park in tight spaces, simulating real-life parking challenges. +- **Parking Maneuvers**: Vehicles are tested on their ability to park in + tight spaces, simulating real-life parking challenges. ## CARLA Maps and Their Traffic Scenarios -CARLA has 12 maps, each designed to test different aspects of self-driving technology, from basic navigation to complex urban and rural situations. +CARLA has 12 maps, each designed to test different aspects of self-driving +technology, from basic navigation to complex urban and rural situations. ### Overview of CARLA Maps -- **Town 1**: A small town with T-junctions and bridges, focusing on managing intersections and staying in lanes. +- **Town 1**: A small town with T-junctions and bridges, focusing on managing + intersections and staying in lanes. -- **Town 2**: Similar to Town 1, this town includes shops and homes, testing navigation and environmental awareness. +- **Town 2**: Similar to Town 1, this town includes shops and homes, testing + navigation and environmental awareness. -- **Town 3**: A larger city area with roundabouts and underpasses, assessing navigation in roundabouts and speed adjustments. +- **Town 3**: A larger city area with roundabouts and underpasses, assessing + navigation in roundabouts and speed adjustments. -- **Town 4**: A small town with a figure-eight road, testing navigation in multiple lanes and interactions with pedestrians. +- **Town 4**: A small town with a figure-eight road, testing navigation in + multiple lanes and interactions with pedestrians. -- **Town 5**: An urban area with multilane roads and a highway, focusing on highway driving and commercial strategies. +- **Town 5**: An urban area with multilane roads and a highway, focusing on + highway driving and commercial strategies. -- **Town 6**: A less populated town with unique junctions, testing navigation in multiple lanes and the use of slip roads. +- **Town 6**: A less populated town with unique junctions, testing navigation + in multiple lanes and the use of slip roads. -- **Town 7**: A rural community with no marked roads, which tests navigation and parking in residential areas. +- **Town 7**: A rural community with no marked roads, which tests navigation + and parking in residential areas. - **Town 8 & Town 9**: Hidden maps used for the Leaderboard challenge. -- **Town 10**: An inner-city area with various junctions and pedestrian activity, testing skills in complex traffic situations. +- **Town 10**: An inner-city area with various junctions and pedestrian + activity, testing skills in complex traffic situations. -- **Town 11**: A basic map for testing navigation in open areas with fewer visual guides. +- **Town 11**: A basic map for testing navigation in open areas with fewer + visual guides. -- **Town 12**: A large map based on Amarillo, Texas, featuring urban, residential, and rural areas to test navigation and visual skills across diverse environments. +- **Town 12**: A large map based on Amarillo, Texas, featuring urban, + residential, and rural areas to test navigation and visual skills across + diverse environments. ### Rural and Highway Scenarios -Maps like **Town 7** and **Town 12** focus on rural and highway driving, simulating open and high-speed areas. They test long-distance driving, speed management, and safe overtaking, requiring drivers to adapt to unmarked roads and agricultural settings. +Maps like **Town 7** and **Town 12** focus on rural and highway driving, +simulating open and high-speed areas. They test long-distance driving, +speed management, and safe overtaking, requiring drivers to adapt to +unmarked roads and agricultural settings. ## Road Network Creation with ASAM OpenDRIVE® -Road networks in CARLA are built using **ASAM OpenDRIVE®**, which standardizes road descriptions: +Road networks in CARLA are built using **ASAM OpenDRIVE®**, which standardizes +road descriptions: -- **Hierarchical Structure**: Roads are organized into nodes for better integration. +- **Hierarchical Structure**: Roads are organized into nodes for better + integration. - **Reference Line**: Each road has a reference line that defines its shape. -- **Interconnectivity**: Roads can connect to each other for realistic traffic flow. +- **Interconnectivity**: Roads can connect to each other for realistic + traffic flow. ## Units of Measurement From b44260392c04eb5012a047084ea48a90068b188c Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 11:13:24 +0100 Subject: [PATCH 06/13] Next attempt to solve Linter Markdowd issues. --- carla_traffic_rules.md | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/carla_traffic_rules.md b/carla_traffic_rules.md index c34c2bc1..bf3eee1c 100644 --- a/carla_traffic_rules.md +++ b/carla_traffic_rules.md @@ -13,11 +13,7 @@ ## Introduction -CARLA (Car Learning to Act) is an open-source simulator designed for testing -and developing self-driving car systems. It incorporates essential traffic -rules to create a flexible environment. Users can modify traffic regulations -and create custom maps, helping them test driving behaviors and interactions -without strictly following the traffic laws of any specific country. +CARLA (Car Learning to Act) is an open-source simulator for testing and developing self-driving car systems. It includes essential traffic rules, creating a flexible environment to test driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. ## Key Traffic Principles and Complex Interactions @@ -53,8 +49,7 @@ without strictly following the traffic laws of any specific country. ## CARLA Maps and Their Traffic Scenarios -CARLA has 12 maps, each designed to test different aspects of self-driving -technology, from basic navigation to complex urban and rural situations. +CARLA has 12 maps, each designed to test different aspects of self-driving, from basic navigation to complex urban and rural situations. Users can also create custom maps, allowing for additional testing scenarios tailored to specific needs. ### Overview of CARLA Maps @@ -62,7 +57,7 @@ technology, from basic navigation to complex urban and rural situations. intersections and staying in lanes. - **Town 2**: Similar to Town 1, this town includes shops and homes, testing - navigation and environmental awareness. + navigation and awareness of the surroundings. - **Town 3**: A larger city area with roundabouts and underpasses, assessing navigation in roundabouts and speed adjustments. From 8fc4dab2dc4df9ed48aa046f6228d6825140ce62 Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 11:16:13 +0100 Subject: [PATCH 07/13] Hopefully solved md issues now. --- carla_traffic_rules.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/carla_traffic_rules.md b/carla_traffic_rules.md index bf3eee1c..589d8cea 100644 --- a/carla_traffic_rules.md +++ b/carla_traffic_rules.md @@ -13,7 +13,8 @@ ## Introduction -CARLA (Car Learning to Act) is an open-source simulator for testing and developing self-driving car systems. It includes essential traffic rules, creating a flexible environment to test driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. +CARLA (Car Learning to Act) is an open-source simulator for testing and developing self-driving car systems. +It includes essential traffic rules, creating a flexible environment to test driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. ## Key Traffic Principles and Complex Interactions From 3a099358e82094e3121fedddb258ac8a18d718b0 Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 11:18:54 +0100 Subject: [PATCH 08/13] One more line break. --- carla_traffic_rules.md | 1 + 1 file changed, 1 insertion(+) diff --git a/carla_traffic_rules.md b/carla_traffic_rules.md index 589d8cea..8da55c1e 100644 --- a/carla_traffic_rules.md +++ b/carla_traffic_rules.md @@ -14,6 +14,7 @@ ## Introduction CARLA (Car Learning to Act) is an open-source simulator for testing and developing self-driving car systems. + It includes essential traffic rules, creating a flexible environment to test driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. ## Key Traffic Principles and Complex Interactions From 46d67daa0875ac6b8cf231e0d27ac6e00ae403b6 Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 11:29:46 +0100 Subject: [PATCH 09/13] Reduced text for fix. --- carla_traffic_rules.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/carla_traffic_rules.md b/carla_traffic_rules.md index 8da55c1e..7bb18519 100644 --- a/carla_traffic_rules.md +++ b/carla_traffic_rules.md @@ -13,9 +13,7 @@ ## Introduction -CARLA (Car Learning to Act) is an open-source simulator for testing and developing self-driving car systems. - -It includes essential traffic rules, creating a flexible environment to test driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. +CARLA (Car Learning to Act) is an open-source simulator for testing self-driving systems. It includes traffic rules, allowing flexible testing of driving behaviors without strict adherence to specific laws of any country. ## Key Traffic Principles and Complex Interactions From b605972ff5bab8f0a0c9aa7bec8f38dd66eedcbf Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Sun, 3 Nov 2024 12:10:33 +0100 Subject: [PATCH 10/13] resolve line too long --- doc/research/paf24/general/carla_traffic_rules.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/doc/research/paf24/general/carla_traffic_rules.md b/doc/research/paf24/general/carla_traffic_rules.md index 7e8fbb2e..3259b8c1 100644 --- a/doc/research/paf24/general/carla_traffic_rules.md +++ b/doc/research/paf24/general/carla_traffic_rules.md @@ -2,18 +2,20 @@ ## Table of Contents +- [Table of Contents](#table-of-contents) - [Introduction](#introduction) - [Key Traffic Principles and Complex Interactions](#key-traffic-principles-and-complex-interactions) - [CARLA Maps and Their Traffic Scenarios](#carla-maps-and-their-traffic-scenarios) - [Overview of CARLA Maps](#overview-of-carla-maps) - [Rural and Highway Scenarios](#rural-and-highway-scenarios) - - [Custom Maps with ASAM OpenDRIVE®](#custom-maps-with-asam-opendrive) +- [Road Network Creation with ASAM OpenDRIVE®](#road-network-creation-with-asam-opendrive) - [Units of Measurement](#units-of-measurement) - [References](#references) ## Introduction -CARLA (Car Learning to Act) is an open-source simulator specifically designed for testing and developing autonomous driving systems. It incorporates essential traffic rules to create a flexible environment. This flexibility allows users to modify traffic regulations and design custom maps, enabling the evaluation of driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. +CARLA (Car Learning to Act) is an open-source simulator specifically designed for testing and developing autonomous driving systems. It incorporates essential traffic rules to create a flexible environment. This flexibility allows users to modify traffic regulations and design custom maps, enabling +the evaluation of driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. ## Key Traffic Principles and Complex Interactions From 0df4628743bd8bcc31c9dcfed5fa3eab03f0f56b Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 14:44:43 +0100 Subject: [PATCH 11/13] Another update for Linter markdown. --- .../paf24/general/carla_traffic_rules.md | 92 ++++++++++++------- 1 file changed, 60 insertions(+), 32 deletions(-) diff --git a/doc/research/paf24/general/carla_traffic_rules.md b/doc/research/paf24/general/carla_traffic_rules.md index 3259b8c1..e06bc78c 100644 --- a/doc/research/paf24/general/carla_traffic_rules.md +++ b/doc/research/paf24/general/carla_traffic_rules.md @@ -2,88 +2,116 @@ ## Table of Contents -- [Table of Contents](#table-of-contents) - [Introduction](#introduction) - [Key Traffic Principles and Complex Interactions](#key-traffic-principles-and-complex-interactions) - [CARLA Maps and Their Traffic Scenarios](#carla-maps-and-their-traffic-scenarios) - [Overview of CARLA Maps](#overview-of-carla-maps) - [Rural and Highway Scenarios](#rural-and-highway-scenarios) -- [Road Network Creation with ASAM OpenDRIVE®](#road-network-creation-with-asam-opendrive) + - [Custom Maps with ASAM OpenDRIVE®](#custom-maps-with-asam-opendrive) - [Units of Measurement](#units-of-measurement) - [References](#references) ## Introduction -CARLA (Car Learning to Act) is an open-source simulator specifically designed for testing and developing autonomous driving systems. It incorporates essential traffic rules to create a flexible environment. This flexibility allows users to modify traffic regulations and design custom maps, enabling -the evaluation of driving behaviors and interactions without strictly adhering to the traffic laws of any specific country. +CARLA (Car Learning to Act) is an open-source simulator for testing and developing autonomous driving systems. It integrates traffic rules to create a flexible environment, letting users adjust regulations and design custom maps to evaluate driving behaviors without following specific national laws. ## Key Traffic Principles and Complex Interactions -- **Traffic Signals**: Vehicles must stop at red lights and proceed on green, facilitating the testing of traffic light detection and timing adjustments. +- **Traffic Signals**: Vehicles must stop at red lights and go on green. -- **Stop Signs**: Vehicles are required to halt at stop signs, yielding to pedestrians and other vehicles, which is essential for ensuring safety in traffic scenarios. +- **Stop Signs**: Vehicles must stop at stop signs and yield to pedestrians + and other cars. -- **Lane Management**: Cars are expected to stay within designated lanes, aiding the development of lane-keeping and lane-following algorithms. +- **Lane Management**: Cars should remain in their designated lanes. -- **Speed Limits**: Varying speed limits across different map zones allow for the testing of speed regulation and adherence to traffic laws. +- **Speed Limits**: Different speed limits in various areas of the map allow + testing of speed control and adherence to traffic laws. -- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, which is crucial for pedestrian detection and ensuring safe braking. +- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, + which is vital for pedestrian detection and ensuring safe stops. -- **Intersection Management**: Vehicles must effectively navigate intersections, managing stop lights and stop signs while yielding as necessary to maintain traffic flow. +- **Intersection Management**: Vehicles must navigate intersections + effectively, managing stop lights and stop signs while yielding when + necessary to keep traffic moving. -- **Roundabout Navigation**: Vehicles are required to yield to cars already within the roundabout, supporting testing of circular navigation and merging strategies. +- **Roundabout Navigation**: Vehicles should yield to cars already in the + roundabout. This tests their ability to navigate circular roads and merge + correctly. -- **Crosswalk Behavior**: Vehicles must detect and stop for pedestrians at crosswalks, highlighting the importance of pedestrian safety. +- **Crosswalk Behavior**: Vehicles need to detect and stop for pedestrians + at crosswalks, highlighting the importance of pedestrian safety. -- **Bicycle Lane Navigation**: Vehicles should be able to detect and navigate around cyclists, ensuring safe interactions in mixed traffic environments. +- **Bicycle Lane Navigation**: Vehicles should be able to detect and safely + navigate around cyclists in traffic. -- **Parking Maneuvers**: Vehicles are tested on their ability to maneuver in tight parking spaces, simulating real-life parking constraints. +- **Parking Maneuvers**: Vehicles are tested on their ability to park in + tight spaces, simulating real-life parking challenges. ## CARLA Maps and Their Traffic Scenarios -CARLA features 12 maps, each designed to test various aspects of autonomous driving, ranging from basic navigation to complex urban and rural settings. +CARLA has 12 maps, each designed to test different aspects of self-driving, +from basic navigation to complex urban and rural situations. +Users can also create custom maps, allowing for additional testing scenarios +tailored to specific needs. ### Overview of CARLA Maps -- **Town 1**: A small town with T-junctions and bridges, focusing on intersection management and lane discipline. +- **Town 1**: A small town with T-junctions and bridges, focusing on managing + intersections and staying in lanes. -- **Town 2**: Similar to Town 1, it includes commercial and residential areas, testing navigation and environmental awareness. +- **Town 2**: Similar to Town 1, this town includes shops and homes, testing + navigation and awareness of the surroundings. -- **Town 3**: A larger urban area with roundabouts and underpasses, assessing roundabout navigation and adaptive speed control. +- **Town 3**: A larger city area with roundabouts and underpasses, assessing + navigation in roundabouts and speed adjustments. -- **Town 4**: A small town with a figure-eight ring road, testing multi-lane navigation and pedestrian interactions. +- **Town 4**: A small town with a figure-eight road, testing navigation in + multiple lanes and interactions with pedestrians. -- **Town 5**: An urban environment featuring multilane roads and a highway, focusing on highway navigation and commercial driving strategies. +- **Town 5**: An urban area with multilane roads and a highway, focusing on + highway driving and commercial strategies. -- **Town 6**: A low-density town with unique junctions, testing multi-lane navigation and slip road utilization. +- **Town 6**: A less populated town with unique junctions, testing navigation + in multiple lanes and the use of slip roads. -- **Town 7**: A rural community with unmarked roads, challenging navigation and parking in residential areas. +- **Town 7**: A rural community with no marked roads, which tests navigation + and parking in residential areas. -- **Town 8 & Town 9**: Secret maps used for the Leaderboard challenge. +- **Town 8 & Town 9**: Hidden maps used for the Leaderboard challenge. -- **Town 10**: An inner-city area with diverse junctions and pedestrian activity, testing negotiation skills in complex traffic. +- **Town 10**: An inner-city area with various junctions and pedestrian + activity, testing skills in complex traffic situations. -- **Town 11**: A minimally decorated map for testing navigation over expansive areas with fewer visual cues. +- **Town 11**: A basic map for testing navigation in open areas with fewer + visual guides. -- **Town 12**: A large map inspired by Amarillo, Texas, with urban, residential, and rural areas, testing navigation and visual perception across diverse environments. +- **Town 12**: A large map based on Amarillo, Texas, featuring urban, + residential, and rural areas to test navigation and visual skills across + diverse environments. ### Rural and Highway Scenarios -Maps like **Town 7** and **Town 12** emphasize rural and highway scenarios, simulating open and high-speed environments. They test long-distance driving, speed regulation, and safe overtaking, requiring adaptability to unmarked roads and agricultural structures in rural settings. +Maps like **Town 7** and **Town 12** focus on rural and highway driving, +simulating open and high-speed areas. They test long-distance driving, +speed management, and safe overtaking, requiring drivers to adapt to +unmarked roads and agricultural settings. ## Road Network Creation with ASAM OpenDRIVE® -Road networks in CARLA are created using **ASAM OpenDRIVE®**, which standardizes road descriptions: +Road networks in CARLA are built using **ASAM OpenDRIVE®**, which standardizes +road descriptions: -- **Hierarchical Structure**: Roads are organized in nodes for better application integration. +- **Hierarchical Structure**: Roads are organized into nodes for better + integration. -- **Reference Line**: Each road has a reference line to define its shape. +- **Reference Line**: Each road has a reference line that defines its shape. -- **Interconnectivity**: Roads can connect for realistic traffic flow. +- **Interconnectivity**: Roads can connect to each other for realistic + traffic flow. ## Units of Measurement -To ensure consistency across tests, CARLA employs standardized units: +To keep tests consistent, CARLA uses standardized units: - **Speed**: Measured in meters per second (m/s). From 40d80965100242176fc375521bc9160176cfc85d Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 14:57:13 +0100 Subject: [PATCH 12/13] "Remove accidentally committed file" --- carla_traffic_rules.md | 121 ----------------------------------------- 1 file changed, 121 deletions(-) delete mode 100644 carla_traffic_rules.md diff --git a/carla_traffic_rules.md b/carla_traffic_rules.md deleted file mode 100644 index 7bb18519..00000000 --- a/carla_traffic_rules.md +++ /dev/null @@ -1,121 +0,0 @@ -# Traffic Rules and Principles in CARLA - -## Table of Contents - -- [Introduction](#introduction) -- [Key Traffic Principles and Complex Interactions](#key-traffic-principles-and-complex-interactions) -- [CARLA Maps and Their Traffic Scenarios](#carla-maps-and-their-traffic-scenarios) - - [Overview of CARLA Maps](#overview-of-carla-maps) - - [Rural and Highway Scenarios](#rural-and-highway-scenarios) - - [Custom Maps with ASAM OpenDRIVE®](#custom-maps-with-asam-opendrive) -- [Units of Measurement](#units-of-measurement) -- [References](#references) - -## Introduction - -CARLA (Car Learning to Act) is an open-source simulator for testing self-driving systems. It includes traffic rules, allowing flexible testing of driving behaviors without strict adherence to specific laws of any country. - -## Key Traffic Principles and Complex Interactions - -- **Traffic Signals**: Vehicles must stop at red lights and go on green. - -- **Stop Signs**: Vehicles must stop at stop signs and yield to pedestrians - and other cars. - -- **Lane Management**: Cars should remain in their designated lanes. - -- **Speed Limits**: Different speed limits in various areas of the map allow - testing of speed control and adherence to traffic laws. - -- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, - which is vital for pedestrian detection and ensuring safe stops. - -- **Intersection Management**: Vehicles must navigate intersections - effectively, managing stop lights and stop signs while yielding when - necessary to keep traffic moving. - -- **Roundabout Navigation**: Vehicles should yield to cars already in the - roundabout. This tests their ability to navigate circular roads and merge - correctly. - -- **Crosswalk Behavior**: Vehicles need to detect and stop for pedestrians - at crosswalks, highlighting the importance of pedestrian safety. - -- **Bicycle Lane Navigation**: Vehicles should be able to detect and safely - navigate around cyclists in traffic. - -- **Parking Maneuvers**: Vehicles are tested on their ability to park in - tight spaces, simulating real-life parking challenges. - -## CARLA Maps and Their Traffic Scenarios - -CARLA has 12 maps, each designed to test different aspects of self-driving, from basic navigation to complex urban and rural situations. Users can also create custom maps, allowing for additional testing scenarios tailored to specific needs. - -### Overview of CARLA Maps - -- **Town 1**: A small town with T-junctions and bridges, focusing on managing - intersections and staying in lanes. - -- **Town 2**: Similar to Town 1, this town includes shops and homes, testing - navigation and awareness of the surroundings. - -- **Town 3**: A larger city area with roundabouts and underpasses, assessing - navigation in roundabouts and speed adjustments. - -- **Town 4**: A small town with a figure-eight road, testing navigation in - multiple lanes and interactions with pedestrians. - -- **Town 5**: An urban area with multilane roads and a highway, focusing on - highway driving and commercial strategies. - -- **Town 6**: A less populated town with unique junctions, testing navigation - in multiple lanes and the use of slip roads. - -- **Town 7**: A rural community with no marked roads, which tests navigation - and parking in residential areas. - -- **Town 8 & Town 9**: Hidden maps used for the Leaderboard challenge. - -- **Town 10**: An inner-city area with various junctions and pedestrian - activity, testing skills in complex traffic situations. - -- **Town 11**: A basic map for testing navigation in open areas with fewer - visual guides. - -- **Town 12**: A large map based on Amarillo, Texas, featuring urban, - residential, and rural areas to test navigation and visual skills across - diverse environments. - -### Rural and Highway Scenarios - -Maps like **Town 7** and **Town 12** focus on rural and highway driving, -simulating open and high-speed areas. They test long-distance driving, -speed management, and safe overtaking, requiring drivers to adapt to -unmarked roads and agricultural settings. - -## Road Network Creation with ASAM OpenDRIVE® - -Road networks in CARLA are built using **ASAM OpenDRIVE®**, which standardizes -road descriptions: - -- **Hierarchical Structure**: Roads are organized into nodes for better - integration. - -- **Reference Line**: Each road has a reference line that defines its shape. - -- **Interconnectivity**: Roads can connect to each other for realistic - traffic flow. - -## Units of Measurement - -To keep tests consistent, CARLA uses standardized units: - -- **Speed**: Measured in meters per second (m/s). - -- **Distance**: Measured in meters. - -## References - -- CARLA Documentation: [CARLA](https://carla.readthedocs.io/en/latest/) - -- ASAM OpenDRIVE® Standard: [ASAM OpenDRIVE](https://www.asam.net/standards/detail/opendrive/) From f0c4f35e74b44efcc2e5e4d152a61689af665f63 Mon Sep 17 00:00:00 2001 From: michalal7 <53532256+michalal7@users.noreply.github.com> Date: Sun, 3 Nov 2024 14:59:05 +0100 Subject: [PATCH 13/13] VSCode Trim Trailing Whitespace --- .../paf24/general/carla_traffic_rules.md | 60 +++++++++---------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/doc/research/paf24/general/carla_traffic_rules.md b/doc/research/paf24/general/carla_traffic_rules.md index e06bc78c..c591879d 100644 --- a/doc/research/paf24/general/carla_traffic_rules.md +++ b/doc/research/paf24/general/carla_traffic_rules.md @@ -19,94 +19,94 @@ CARLA (Car Learning to Act) is an open-source simulator for testing and developi - **Traffic Signals**: Vehicles must stop at red lights and go on green. -- **Stop Signs**: Vehicles must stop at stop signs and yield to pedestrians +- **Stop Signs**: Vehicles must stop at stop signs and yield to pedestrians and other cars. - **Lane Management**: Cars should remain in their designated lanes. -- **Speed Limits**: Different speed limits in various areas of the map allow +- **Speed Limits**: Different speed limits in various areas of the map allow testing of speed control and adherence to traffic laws. -- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, +- **Pedestrian Rights**: Vehicles must yield to pedestrians at crosswalks, which is vital for pedestrian detection and ensuring safe stops. -- **Intersection Management**: Vehicles must navigate intersections - effectively, managing stop lights and stop signs while yielding when +- **Intersection Management**: Vehicles must navigate intersections + effectively, managing stop lights and stop signs while yielding when necessary to keep traffic moving. -- **Roundabout Navigation**: Vehicles should yield to cars already in the - roundabout. This tests their ability to navigate circular roads and merge +- **Roundabout Navigation**: Vehicles should yield to cars already in the + roundabout. This tests their ability to navigate circular roads and merge correctly. -- **Crosswalk Behavior**: Vehicles need to detect and stop for pedestrians +- **Crosswalk Behavior**: Vehicles need to detect and stop for pedestrians at crosswalks, highlighting the importance of pedestrian safety. -- **Bicycle Lane Navigation**: Vehicles should be able to detect and safely +- **Bicycle Lane Navigation**: Vehicles should be able to detect and safely navigate around cyclists in traffic. -- **Parking Maneuvers**: Vehicles are tested on their ability to park in +- **Parking Maneuvers**: Vehicles are tested on their ability to park in tight spaces, simulating real-life parking challenges. ## CARLA Maps and Their Traffic Scenarios -CARLA has 12 maps, each designed to test different aspects of self-driving, -from basic navigation to complex urban and rural situations. -Users can also create custom maps, allowing for additional testing scenarios +CARLA has 12 maps, each designed to test different aspects of self-driving, +from basic navigation to complex urban and rural situations. +Users can also create custom maps, allowing for additional testing scenarios tailored to specific needs. ### Overview of CARLA Maps -- **Town 1**: A small town with T-junctions and bridges, focusing on managing +- **Town 1**: A small town with T-junctions and bridges, focusing on managing intersections and staying in lanes. -- **Town 2**: Similar to Town 1, this town includes shops and homes, testing +- **Town 2**: Similar to Town 1, this town includes shops and homes, testing navigation and awareness of the surroundings. -- **Town 3**: A larger city area with roundabouts and underpasses, assessing +- **Town 3**: A larger city area with roundabouts and underpasses, assessing navigation in roundabouts and speed adjustments. -- **Town 4**: A small town with a figure-eight road, testing navigation in +- **Town 4**: A small town with a figure-eight road, testing navigation in multiple lanes and interactions with pedestrians. -- **Town 5**: An urban area with multilane roads and a highway, focusing on +- **Town 5**: An urban area with multilane roads and a highway, focusing on highway driving and commercial strategies. -- **Town 6**: A less populated town with unique junctions, testing navigation +- **Town 6**: A less populated town with unique junctions, testing navigation in multiple lanes and the use of slip roads. -- **Town 7**: A rural community with no marked roads, which tests navigation +- **Town 7**: A rural community with no marked roads, which tests navigation and parking in residential areas. - **Town 8 & Town 9**: Hidden maps used for the Leaderboard challenge. -- **Town 10**: An inner-city area with various junctions and pedestrian +- **Town 10**: An inner-city area with various junctions and pedestrian activity, testing skills in complex traffic situations. -- **Town 11**: A basic map for testing navigation in open areas with fewer +- **Town 11**: A basic map for testing navigation in open areas with fewer visual guides. -- **Town 12**: A large map based on Amarillo, Texas, featuring urban, - residential, and rural areas to test navigation and visual skills across +- **Town 12**: A large map based on Amarillo, Texas, featuring urban, + residential, and rural areas to test navigation and visual skills across diverse environments. ### Rural and Highway Scenarios -Maps like **Town 7** and **Town 12** focus on rural and highway driving, -simulating open and high-speed areas. They test long-distance driving, -speed management, and safe overtaking, requiring drivers to adapt to +Maps like **Town 7** and **Town 12** focus on rural and highway driving, +simulating open and high-speed areas. They test long-distance driving, +speed management, and safe overtaking, requiring drivers to adapt to unmarked roads and agricultural settings. ## Road Network Creation with ASAM OpenDRIVE® -Road networks in CARLA are built using **ASAM OpenDRIVE®**, which standardizes +Road networks in CARLA are built using **ASAM OpenDRIVE®**, which standardizes road descriptions: -- **Hierarchical Structure**: Roads are organized into nodes for better +- **Hierarchical Structure**: Roads are organized into nodes for better integration. - **Reference Line**: Each road has a reference line that defines its shape. -- **Interconnectivity**: Roads can connect to each other for realistic +- **Interconnectivity**: Roads can connect to each other for realistic traffic flow. ## Units of Measurement