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].

Market Performance - Model Y sales are 3 times stronger than the next best non Tesla model [1] - Tesla is the best-selling brand in Europe [1] - Model Y is the best-selling model in Europe [1] - Model 3 is the 2nd best-selling model in Europe [1]

以下文章来源于晚点Auto ，作者晚点团队 晚点Auto . 从制造到创造，从不可能到可能。《晚点LatePost》旗下汽车品牌。 目前主要服务北汽、上汽大通等车企。 文丨赵宇 编辑丨 程曼祺 我们独家获悉，智能驾驶供应商易航智能近日完成数亿元 C 轮融资，由北汽产投、浙江金控投资公司、德 清产投、财通资本联合投资。其中，浙江金控投资公司为浙江省级投资平台，德清产投为湖州德清县级投 资平台。 北汽在 2022 年与易航智能达成合作，主打越野车的北汽 BJ40 系列车型已搭载易航智能的 L2 级前视一体 机（集成摄像头等传感器的硬件套件）和 L2+ 级高速 NOA 方案。 易航智能称，本轮融资后，易航智能或将为北汽集团旗下 BEIJING、极狐品牌的车型开发智驾方案。 浙江德清县政府则正在招引智驾项目，打造智能驾驶示范区。蔚来激光雷达供应商图达通的一条产线和港口 无人驾驶公司斯年智驾总部基地已落地德清。 易航智能由陈禹行于 2015 年 8 月创立。他博士毕业于吉林大学车辆工程专业，师从中国工程院院士郭孔辉， 郭孔辉也是空气悬架公司浙江孔辉的前身 "长春孔辉" 的创始人；在美国加州伯克利大学交流期间，陈禹行还 ...