Autonomous Driving Benchmark

Autonomous driving benchmarks are crucial for evaluating and improving the performance of self-driving systems, focusing on realistic scenarios and diverse challenges. Current research emphasizes developing comprehensive simulation platforms and datasets, often incorporating real-world driving data and leveraging reinforcement learning (RL) and imitation learning (IL) algorithms, including transformer-based models for perception and control. These benchmarks are vital for advancing the field by providing standardized evaluation metrics and facilitating the development of more robust and reliable autonomous driving technologies. The availability of publicly accessible datasets and codebases fosters collaboration and accelerates progress.