Car Learning to Act

"Car Learning to Act" focuses on training autonomous vehicles (AVs) using simulation, primarily leveraging the CARLA simulator, to achieve safe and reliable navigation in complex and unpredictable real-world scenarios. Current research emphasizes developing robust perception models (often using vision-language models or deep reinforcement learning) capable of handling diverse weather conditions, sensor failures, and unusual traffic behaviors, often using techniques like data augmentation and adversarial testing to improve model robustness. This research is crucial for advancing the safety and reliability of AVs, providing a controlled environment for rigorous testing and validation before deployment on public roads.