CARLA Simulator

Step 1: Create a scenario with a lead vehicle and the ego vehicle approaching it.

Step 2: Configure the lead vehicle to suddenly brake.

Step 3: Monitor the ego vehicle's AEB system response using CARLA's sensor data.

Step 4: Evaluate the AEB system's performance based on collision avoidance and deceleration metrics.

Step 1: Generate a map with a roundabout using the OpenDRIVE standard.

Step 2: Spawn multiple vehicles to create realistic traffic flow in the roundabout.

Step 3: Train the autonomous vehicle's navigation system using reinforcement learning techniques within CARLA.

Step 4: Evaluate the vehicle's ability to safely and efficiently navigate the roundabout.

Step 1: Configure the simulation environment with specific weather conditions (e.g., heavy rain, dense fog).

Step 2: Simulate sensor data from cameras, LiDARs, and radars under these conditions.

Step 3: Analyze the degradation in sensor performance (e.g., reduced detection range, increased noise).

Step 4: Develop sensor fusion algorithms to mitigate the effects of adverse weather.

Step 1: Create a scenario with pedestrians crossing the road at designated crosswalks.

Step 2: Vary pedestrian behavior (e.g., walking speed, jaywalking) to create diverse situations.

Step 3: Train the autonomous vehicle's perception system to accurately detect pedestrians.

Step 4: Evaluate the vehicle's response to pedestrian crossings, ensuring safe braking and avoidance.

Step 1: Generate a highway map with clear lane markings.

Step 2: Configure the autonomous vehicle with a lane keeping assist system.

Step 3: Simulate various driving conditions (e.g., straight road, curves, wind gusts).

Step 4: Evaluate the lane keeping system's performance by monitoring lane deviation and steering corrections.