https://xuquant.com/categories/autonomous-driving/Recent content in Autonomous Driving on Xu'Bloghttps://xuquant.com/images/profile.jpghttps://xuquant.com/images/profile.jpgHugo -- 0.152.2enFri, 08 May 2026 18:00:00 +0800https://xuquant.com/posts/reflectdrive-2-discrete-diffusion-end-to-end-driving/Fri, 08 May 2026 18:00:00 +0800https://xuquant.com/posts/reflectdrive-2-discrete-diffusion-end-to-end-driving/深度解读理想汽车 ReflectDrive-2：首创离散扩散模型用于端到端自动驾驶规划，提出「决策-起草-反思」三阶段推理范式，通过强化学习联合优化起草+编辑实现 AutoEdit 增益放大 6 倍，纯相机输入达 NAVSIM SOTA 91.0 PDMS，Thor 芯片上 31.8ms/帧实时部署。https://xuquant.com/posts/xpeng-x-cache-world-model-inference-acceleration/Thu, 07 May 2026 18:00:00 +0800https://xuquant.com/posts/xpeng-x-cache-world-model-inference-acceleration/深度解读小鹏 X-Cache：通过跨段残差缓存实现世界模型 2.7 倍推理加速，71% DiT block 跳过率且几乎零画质损失，training-free 的自动驾驶推理优化方案。https://xuquant.com/posts/autodrive/basic_rl/Tue, 20 Jan 2026 10:00:00 +0800https://xuquant.com/posts/autodrive/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/posts/autodrive/nvidia_vla/Sun, 11 Jan 2026 10:00:00 +0800https://xuquant.com/posts/autodrive/nvidia_vla/Technical deep-dive into Nvidia's Cosmos-Reason (Alpamayo) VLA system for autonomous driving, covering tri-plane vision encoding, ego-shortcut avoidance, Cause-of-Change dataset paradigm, and reasoning-action alignment via reinforcement learning.https://xuquant.com/posts/autodrive/e2e-autonomous-driving-evolution/Thu, 01 May 2025 10:00:00 +0800https://xuquant.com/posts/autodrive/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/posts/autodrive/rl-policy-optimization-e2e-driving/Wed, 30 Apr 2025 10:00:00 +0800https://xuquant.com/posts/autodrive/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/posts/autodrive/ar-trajectory-tokenization/Tue, 01 Apr 2025 10:00:00 +0800https://xuquant.com/posts/autodrive/ar-trajectory-tokenization/A deep dive into trajectory tokenization for autoregressive driving planners: from state-based discretization via k-means clustering, through token matching and reconstruction, to the AR+Diffusion paradigm and GRPO-based reinforcement learning post-training.https://xuquant.com/posts/autodrive/generative-planning-nonconvex/Sat, 15 Mar 2025 10:00:00 +0800https://xuquant.com/posts/autodrive/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.