English(EN)End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems
新研究探索用于自动驾驶安全与规划的高级AI · 追踪10个来源
作者PulseAugur 编辑部·[11 个来源]·
arXiv上发表的多篇研究论文探讨了自动驾驶系统的先进技术，重点是改进规划、预测和安全。其中一篇论文介绍了一个分层评估协议，用于评估生成场景模型的物理一致性，揭示了标准指标未显现的局限性。另一篇论文提出了Diffusion-2BC，一种用于离线行为克隆的混合扩散和回归训练方法，可提高闭环性能。此外，对规则对齐扩散规划器（RADP）的研究旨在将驾驶规则直接纳入扩散模型，以提高可解释性和安全性，同时正在开发Meta-多智能体强化学习（meta-MARL）框架，以实现交互式策略的快速适应。其他研究则侧重于高效的多模态规划、物理一致的世界动作模型以及利用车路通信的协同世界动作模型。
AI
arXiv:2610.01581v1 Announce Type: new Abstract: Generative AI models are increasingly used for scenario generation in autonomous driving. While they can generate realistic-looking scenarios, they often provide limited transparency into learned representations and consistency with…
arXiv cs.LG
TIER_1English(EN)·Bruno Maciel Machado, Eric Aislan Antonelo·
arXiv:2609.38472v1 Announce Type: cross Abstract: Behavior cloning provides an offline route to autonomous-driving policy learning, but mean-squared-error regression is poorly matched to demonstrations in which one observation admits several valid actions. Diffusion policies can …
arXiv cs.LG
TIER_1English(EN)·Jiaxi Ye, Chunji Lv, Guoren Wang, Changsheng Li·
arXiv:2609.39995v2 Announce Type: new Abstract: Diffusion planners exhibit strong capabilities in generating multimodal trajectories. However, existing methods primarily rely on expert demonstrations to fit trajectory distributions, learning statistical correlations among scenes,…
arXiv cs.AI
TIER_1English(EN)·Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu·
arXiv:2610.00705v1 Announce Type: new Abstract: This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly ada…
arXiv:2609.38862v1 Announce Type: cross Abstract: Safe and efficient trajectory planning is essential in autonomous driving. However, existing end-to-end approaches often fall short in both computational efficiency and safety guarantees. Methods based on imitation learning suffer…
This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level op…
arXiv:2610.00926v1 Announce Type: cross Abstract: Autonomous driving is a cornerstone technology for the future of intelligent transportation, where end-to-end learning has emerged as a transformative paradigm that directly maps multimodal sensory inputs to driving actions throug…
arXiv:2610.01746v1 Announce Type: cross Abstract: Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures offering two prominent design paradigms for their implementation. E2E Learning uses …
arXiv cs.CV
TIER_1English(EN)·Dhruv Parikh, Fengcheng Yu, Quankai Gao, Jiawei Yang, Junjie Ye, Maulik Bhatt, Thang Vu, Charles Ochoa, Rowan McAllister, Igor Vasiljevic, Rajgopal Kannan, Viktor Prasanna, Vitor Guizilini, Yue Wang·
arXiv:2609.37970v1 Announce Type: cross Abstract: World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily impose a shared geometric constraint on these predictions. We present PhysWAM, a u…
arXiv cs.CV
TIER_1English(EN)·Junwei You, Weizhe Tang, Can Wang, Yan Zhao, Jun Hua, Haotian Shi, Wei Zhang, Lin Wang, Bin Ran·
arXiv:2609.37098v1 Announce Type: cross Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providing valuable support for end-to-end autonomous driving. However, existing cooperati…
arXiv:2609.36851v1 Announce Type: new Abstract: End-to-end autonomous driving policies are commonly trained via imitation learning on logged demonstrations without observing the consequences of their own actions, leading to causal confusion in closed-loop real-world deployment. T…
