自动驾驶一周论文精选！端到端、VLA、感知、决策等~

Core Viewpoint - The article emphasizes the recent advancements in autonomous driving research, highlighting various innovative approaches and frameworks that enhance the capabilities of autonomous systems in dynamic environments [2][4]. Group 1: End-to-End Autonomous Driving - The article discusses several notable papers focusing on end-to-end autonomous driving, including GMF-Drive, ME³-BEV, SpaRC-AD, IRL-VLA, and EvaDrive, which utilize advanced techniques such as gated fusion, deep reinforcement learning, and evolutionary adversarial strategies [8][10]. Group 2: Perception and VLM - The VISTA paper introduces a vision-language model for predicting driver attention in dynamic environments, showcasing the integration of visual and language processing for improved situational awareness [7]. - The article also mentions the development of safety-critical perception technologies, such as the progressive BEV perception survey and the CBDES MoE model for functional module decoupling [10]. Group 3: Simulation Testing - The article highlights the ReconDreamer-RL framework, which enhances reinforcement learning through diffusion-based scene reconstruction, indicating a trend towards more sophisticated simulation testing methodologies [11]. Group 4: Datasets - The STRIDE-QA dataset is introduced as a large-scale visual question answering resource aimed at spatiotemporal reasoning in urban driving scenarios, reflecting the growing need for comprehensive datasets in autonomous driving research [12].