Core Insights - The article discusses the advancements in end-to-end (end2end) autonomous driving systems, highlighting the prominence of Behavior-Driven End-to-End (BEV) frameworks while noting the ongoing challenges in planning due to interaction modeling complexities [2][40]. Group 1: Interaction Modeling - Interaction modeling is identified as a critical area in planning, involving game theory and uncertainty modeling, which current supervised learning methods struggle to address effectively [2][5]. - The report emphasizes the importance of incorporating ego and agent trajectories into loss functions or constraints to enhance planning outcomes [2][5]. Group 2: Planning Frameworks - Various frameworks for interactive planning are discussed, including POMDP, contingency planners, and game theory approaches, focusing on how to integrate interaction within the planning pipeline [5][40]. - The article outlines a typical interactive planning process that includes perturbing ego trajectories, predicting all agents' movements, and employing dynamic programming to derive optimal policies [6][12]. Group 3: Loss Functions and Constraints - The loss function for planning is detailed, incorporating terms for collision avoidance between ego and agent trajectories, with specific components for prediction accuracy and collision penalties [9][16]. - The article explains how interaction is modeled within the loss function, ensuring that agent predictions do not lead to collisions with the ego vehicle [9][16]. Group 4: Real-Time Optimization - The article discusses latency issues in planning and proposes using Alternating Direction Method of Multipliers (ADMM) to achieve real-time performance, achieving up to 125Hz with multiple agents [19][18]. - It highlights the need for efficient optimization techniques to reduce computation time, with a focus on achieving real-time capabilities in autonomous driving systems [19][18]. Group 5: Future Considerations - The article raises questions about the effectiveness of prediction-oriented methods in dynamic scenarios, suggesting that these methods may not adequately address counterfactual situations where agent behavior diverges from predictions [41][42]. - It discusses the necessity for improved prediction models and the potential for modular frameworks to enhance trajectory optimization in autonomous vehicles [45][44].