Updated carla_traffic_rules.md

doc/research/paf24/general/carla_traffic_rules.md

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 # 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/)