3 accepted papers
Safe and feasible trajectory planning is critical for real-world autonomous driving systems. However, existing learning-based planners rely heavily on expert demonstrations, which not only lack explicit safety awareness but also risk inheriting undesirable behaviors such as speeding from suboptimal…
Planning the trajectory for an autonomous vehicle (AV) requires considering both the AV's objectives and the behaviors of surrounding agents, which interact in a dynamic process. Joint prediction and planning (JPP) methods have been proposed to model this bidirectional interaction and generate compa
Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm which predicts the future trajectory by using a fixed duration of historical frames. These methods make the predictions independently even at adjacent time s…