https://xuquant.com/en/tags/e2e-driving/Recent content in E2E-Driving on Xu'Bloghttps://xuquant.com/og-default.pnghttps://xuquant.com/og-default.pngHugo -- 0.152.2enSat, 20 Sep 2025 10:00:00 +0800https://xuquant.com/en/posts/autonomous-driving/basic_rl/Sat, 20 Sep 2025 10:00:00 +0800https://xuquant.com/en/posts/autonomous-driving/basic_rl/Comprehensive analysis of applying reinforcement learning to end-to-end autonomous driving, covering metric caching, Direct Preference Optimization (DPO) across action representations, and strategies for breaking sampling ceilings in iterative self-improvement.https://xuquant.com/en/posts/autonomous-driving/nvidia_vla/Sat, 30 Aug 2025 10:00:00 +0800https://xuquant.com/en/posts/autonomous-driving/nvidia_vla/Technical deep-dive into Nvidia's Alpamayo VLA system for autonomous driving, built on the Cosmos-Reason VLM backbone, covering tri-plane vision encoding, ego-shortcut avoidance, Cause-of-Change dataset paradigm, and reasoning-action alignment via reinforcement learning.https://xuquant.com/en/posts/autonomous-driving/rl-policy-optimization-e2e-driving/Sat, 09 Aug 2025 10:00:00 +0800https://xuquant.com/en/posts/autonomous-driving/rl-policy-optimization-e2e-driving/A systematic derivation of policy optimization methods for end-to-end autonomous driving: from REINFORCE through PPO to GRPO, covering advantage estimation, sampling differences between LLM and driving, multi-objective loss design, and the role of noise in diffusion-based exploration.https://xuquant.com/en/posts/autonomous-driving/e2e-autonomous-driving-evolution/Sat, 19 Jul 2025 10:00:00 +0800https://xuquant.com/en/posts/autonomous-driving/e2e-autonomous-driving-evolution/A technical survey on the architectural evolution of end-to-end autonomous driving, covering planner decoder selection (AR vs Diffusion vs Flow Matching), VLA integration strategies, and engineering best practices for data infrastructure, training optimization, and evaluation systems.https://xuquant.com/en/posts/autonomous-driving/generative-planning-nonconvex/Sat, 07 Jun 2025 10:00:00 +0800https://xuquant.com/en/posts/autonomous-driving/generative-planning-nonconvex/A first-principles analysis of why regression-based planners fail in autonomous driving: the feasible set is non-convex, MSE averages into obstacles, GMM is a patch not a solution, and generative approaches are necessary.