Highway Driving

Highway driving research focuses on developing safe and efficient autonomous driving systems, encompassing perception, decision-making, and control. Current efforts concentrate on improving model accuracy and generalization using techniques like deep reinforcement learning, transformer networks, and advanced loss functions within various architectures (e.g., U-nets, Decision Transformers). These advancements aim to enhance aspects such as collision avoidance, trajectory prediction, and adherence to traffic laws, ultimately contributing to improved road safety and traffic management. The development of high-fidelity simulators and large-scale datasets is also crucial for training and validating these models.