Reliable Autonomous Driving

Reliable autonomous driving necessitates robust systems capable of safe and efficient navigation in diverse, unpredictable environments. Current research emphasizes improving the reliability of perception models (e.g., using 3D tokenized LLMs and enhanced activation functions for resilience against hardware faults), optimizing decision-making strategies (e.g., through reinforcement learning and hyperparameter optimization), and developing comprehensive safety mechanisms (e.g., behavior trees for functional safety supervision and vulnerability-adaptive protection paradigms). These advancements aim to address limitations in existing autonomous driving systems, ultimately contributing to safer and more dependable vehicles on public roads.