Advancing Autonomous Driving through Simulation

This chapter explores the evolution of driving models influenced by expert demonstrations and various datasets. It delves into the challenges of on-policy versus off-policy learning, presenting innovative simulation strategies to enhance training effectiveness. The discussion highlights the integration of world models in simulating real-world scenarios, addressing complexities such as trajectory planning, recovery behaviors, and the utilization of diverse data for model training.