自动驾驶

PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving

Thibault Buhet, Emilie Wirbel, Andrei Bursuc, Xavier Perrotton

专题命中 规划控制 ：autonomous driving(title)；trajectory planning(title)；LiDAR(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Accepted at CorRL 2020 (matching camera-ready version)

Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving

Shota Yamazaki, Chenyu Zhang, Takuya Nanri, Akio Shigekane, Siyuan Wang, Jo Nishiyama, Tao Chu, Kohei Yokosawa

专题命中 规划控制 ：autonomous driving(title,abstract)；trajectory planning(title)；分类 cs.RO、cs.CV

Comments Accepted and presented at ECCV 2024 2nd Workshop on Vision-Centric Autonomous Driving (VCAD) on September 30, 2024. 13 pages, 5 figures

A LiDAR Assisted Control Module with High Precision in Parking Scenarios for Autonomous Driving Vehicle

Xin Xu, Yu Dong, Fan Zhu

专题命中 规划控制 ：autonomous driving(title,abstract)；LiDAR(title)；分类 cs.RO、cs.AI

Less is More: Lean yet Powerful Vision-Language Model for Autonomous Driving

少即是多：一种高效而强大的视觉-语言模型用于自动驾驶

Sheng Yang, Tong Zhan, Guancheng Chen, Yanfeng Lu, Jian Wang

机构 * School of Data Science, Fudan University Shanghai, China（复旦大学数据科学学院） ； Institute of Automation, Chinese Academy of Sciences Beijing, China（中国科学院自动化研究所） 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；self-driving(abstract)；trajectory planning(abstract)；分类 cs.RO、cs.CV、cs.AI

AI总结 本研究提出Max-V1模型，通过端到端视觉-语言方法实现高效自动驾驶，取得nuScenes数据集上的显著性能提升。

MTR-VP: Towards End-to-End Trajectory Planning through Context-Driven Image Encoding and Multiple Trajectory Prediction

MTR-VP: 通过基于上下文的图像编码和多轨迹预测实现端到端轨迹规划

Maitrayee Keskar, Mohan Trivedi, Ross Greer

机构 * Machine Intelligence, Interaction, and Imagination (Mi 3 ) Laboratory（机器智能、交互与想象实验室） ； University of California, Merced（加州大学默塞德分校） ； Laboratory for Intelligent & Safe Automobiles (LISA)（智能与安全汽车实验室） ； University of California, San Diego（加州大学圣地亚哥分校） 纠错

专题命中 规划控制 ：trajectory planning(title,abstract)；autonomous driving(abstract)；end-to-end driving(abstract)；分类 cs.RO、cs.CV、cs.AI

AI总结 MTR-VP通过基于上下文的图像编码和多轨迹预测实现端到端轨迹规划，利用交叉注意力提升规划性能。

Comments 8 pages, 3 figures, 4 tables

FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Tengju Ye, Wei Jing, Chunyong Hu, Shikun Huang, Lingping Gao, Fangzhen Li, Jingke Wang, Ke Guo, Wencong Xiao, Weibo Mao, Hang Zheng, Kun Li, Junbo Chen, Kaicheng Yu

专题命中 规划控制 ：autonomous driving(title,abstract)；LiDAR(abstract)；occupancy(abstract)；分类 cs.RO、cs.CV、cs.AI

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

Chiho Choi, Joon Hee Choi, Jiachen Li, Srikanth Malla

专题命中 规划控制 ：autonomous driving(title,abstract)；self-driving(abstract)；LiDAR(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments CVPR 2021 [Oral]

ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving

ColaVLA: 借助认知潜在推理实现自动驾驶中的分层并行轨迹规划

Qihang Peng, Xuesong Chen, Chenye Yang, Shaoshuai Shi, Hongsheng Li

机构 * Tsinghua University（清华大学） ； CUHK MMLab（香港中文大学MMLab） ； Voyager Research, Didi Chuxing（Voyager Research，滴滴出行） 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；trajectory planning(title)；分类 cs.CV

AI总结 ColaVLA通过统一视觉-语言-动作框架，解决自动驾驶中轨迹规划的效率与准确性问题，实现高效、安全的多尺度轨迹生成。

Comments CVPR2026(Main Conference). Project page: https://pqh22.github.io/projects/ColaVLA/index.html

DiffRefiner: Coarse to Fine Trajectory Planning via Diffusion Refinement with Semantic Interaction for End to End Autonomous Driving

DiffRefiner: 通过扩散细化与语义交互实现从粗到细的轨迹规划用于端到端自动驾驶

Liuhan Yin, Runkun Ju, Guodong Guo, Erkang Cheng

机构 * Nullmax 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；trajectory planning(title)；分类 cs.CV

AI总结 DiffRefiner通过两阶段框架结合扩散细化与语义交互，实现更精确的端到端自动驾驶轨迹规划，取得新纪录。

Comments Accepted to AAAI 2026

Occupancy-aware Trajectory Planning for Autonomous Valet Parking in Uncertain Dynamic Environments

Farhad Nawaz, Faizan M. Tariq, Sangjae Bae, David Isele, Avinash Singh, Nadia Figueroa, Nikolai Matni, Jovin D'sa

机构 * Honda Research Institute (HRI)（本田研究院） ； University of Pennsylvania（宾夕法尼亚大学） 纠错

专题命中 规划控制 ：occupancy(title,abstract)；trajectory planning(title)；分类 cs.RO

AeroTraj: Trajectory Planning for Fast, and Accurate 3D Reconstruction Using a Drone-based LiDAR

Fawad Ahmad, Christina Shin, Rajrup Ghosh, John D'Ambrosio, Eugene Chai, Karthik Sundaresan, Ramesh Govindan

专题命中 规划控制 ：LiDAR(title,abstract)；trajectory planning(title)；分类 cs.RO

Trajectory Planning for Autonomous Driving in Unstructured Scenarios Based on Graph Neural Network and Numerical Optimization

Sumin Zhang, Kuo Li, Rui He, Zhiwei Meng, Yupeng Chang, Xiaosong Jin, Ri Bai

专题命中 规划控制 ：trajectory planning(title,abstract)；autonomous driving(title)；分类 cs.RO

Adaptive Spatio-Temporal Voxels Based Trajectory Planning for Autonomous Driving in Highway Traffic Flow

Zhiqiang Jian, Songyi Zhang, Lingfeng Sun, Wei Zhan, Masayoshi Tomizuka, Nanning Zheng

专题命中 规划控制 ：trajectory planning(title,abstract)；autonomous driving(title)；分类 cs.RO

Comments 8 pages, 5 figures

Journal ref IEEE ITSC 2023

Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning

Xinyang Lu, Flint Xiaofeng Fan, Tianying Wang

专题命中 规划控制 ：autonomous driving(title)；trajectory planning(title)；LiDAR(abstract)；分类 cs.RO

Comments ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems

Real-Time Unified Trajectory Planning and Optimal Control for Urban Autonomous Driving Under Static and Dynamic Obstacle Constraints

Rowan Dempster, Mohammad Al-Sharman, Derek Rayside, William Melek

专题命中 规划控制 ：trajectory planning(title,abstract)；autonomous driving(title)；分类 cs.RO

Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles

Abbas Sadat, Mengye Ren, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun

专题命中 规划控制 ：self-driving(title,abstract)；trajectory planning(title)；分类 cs.RO

Comments IROS 2019

Hierarchical Trajectory Planning for Autonomous Driving in Low-speed Driving Scenarios Based on RRT and Optimization

Yuying Chen, Haoyang Ye, Ming Liu

专题命中 规划控制 ：trajectory planning(title,abstract)；autonomous driving(title)；分类 cs.RO

Comments 8 pages, 12 figures

Learning Direct Control Policies with Flow Matching for Autonomous Driving

通过流匹配学习自动驾驶的直接控制策略

Marcello Ceresini, Federico Pirazzoli, Andrea Bertogalli, Lorenzo Cipelli, Filippo D'Addeo, Anthony Dell'Eva, Alessandro Paolo Capasso, Alberto Broggi

机构 * Università degli Studi di Parma（帕尔马大学） ； Alma Mater Studiorum - Università di Bologna（博洛尼亚大学） ； VisLab (an Ambarella Inc. company)（VisLab（安柏莱拉公司）） 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；BEV(abstract,abstract_cn)；分类 cs.RO、cs.CV

AI总结 本文提出一种基于流匹配的自动驾驶规划器，直接输出由加速度和曲率剖面定义的可控轨迹，通过鸟瞰图栅格输入，在少量常微分方程积分步骤中生成控制序列，实现低延迟推理。在真实城市场景上训练并在多车道高速公路等新环境中评估，证明模型能可靠泛化。

Comments 16 pages, 6 figures, 2 tables. Accepted for oral presentation at the 2026 IEEE International Conference on Intelligent Transportation Systems (ITSC 2026)

LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving

LWDrive: 基于逐层世界模型的视觉-语言模型自动驾驶规划

Chen Yang, Yuhao Wei, Ze Xu, Ziheng Zou, Shuang Liang, Delin Ouyang, Lingfeng Qi, Jie Li, Guofa Li

机构 * Chongqing University（重庆大学） 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；BEV(abstract,abstract_cn)；分类 cs.CV、cs.AI

AI总结 提出LWDrive框架，通过逐层世界模型引导，将VLM输出的粗轨迹逐步细化为几何精确、多视角感知的规划轨迹，在NAVSIM基准上取得92.0分。

SparseOccVLA: Bridging Occupancy and Vision-Language Models via Sparse Queries for Unified 4D Scene Understanding and Planning

SparseOccVLA: 通过稀疏查询弥合占用与视觉语言模型之间的鸿沟，实现统一的4D场景理解和规划

Chenxu Dang, Jie Wang, Guang Li, Zhiwen Hou, Zihan You, Hangjun Ye, Jie Ma, Long Chen, Yan Wang

机构 * Huazhong University of Science and Technology（华中科技大学） ； Xiaomi EV（小米电动车） ； Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院） 纠错

专题命中 规划控制 ：occupancy(title,abstract)；autonomous driving(abstract)；trajectory planning(abstract)；分类 cs.CV、cs.AI

AI总结 SparseOccVLA通过稀疏查询整合视觉语言模型与语义占用，实现统一的4D场景理解和规划，提升自动驾驶性能。

SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving

SUPER-AD：语义不确定性感知的端到端鲁棒自动驾驶规划

Wonjeong Ryu, Seungjun Yu, Seokha Moon, Hojun Choi, Junsung Park, Jinkyu Kim, Hyunjung Shim

机构 * KAIST AI（韩国科学技术院人工智能研究所） ； Korea University（韩国大学） 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；BEV(abstract)；trajectory planning(abstract)；分类 cs.RO、cs.CV

AI总结 SUPER-AD通过语义不确定性感知和驾驶先验知识，提升端到端自动驾驶在复杂不确定性条件下的安全性和可靠性。

VAD: Vectorized Scene Representation for Efficient Autonomous Driving

Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, Xinggang Wang

专题命中 规划控制 ：autonomous driving(title,abstract)；occupancy(abstract)；trajectory planning(abstract)；分类 cs.RO、cs.CV

Comments Accepted to ICCV 2023. Code&Demos: https://github.com/hustvl/VAD

Shared Cross-Modal Trajectory Prediction for Autonomous Driving

Chiho Choi, Joon Hee Choi, Srikanth Malla, Jiachen Li

专题命中 规划控制 ：autonomous driving(title,abstract)；self-driving(abstract)；LiDAR(abstract)；分类 cs.RO、cs.CV

Comments CVPR 2021 [Oral]. arXiv admin note: substantial text overlap with arXiv:2011.08436

An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning

Yuchen Sun, Dongchun Ren, Shiqi Lian, Mingyu Fan, Xiangyi Teng

专题命中 规划控制 ：trajectory planning(title,abstract)；autonomous driving(abstract)；self-driving(abstract)；分类 cs.RO、cs.AI

Comments no comments

A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning

Majid Moghadam, Gabriel Hugh Elkaim

专题命中 规划控制 ：autonomous driving(title,abstract)；self-driving(abstract)；occupancy(abstract)；分类 cs.RO、cs.AI

Comments Appears in ICML 2019 workshop on Real-world Sequential Decision Making: Reinforcement Learning and Beyond. Source code available in: https://github.com/MajidMoghadam2006

ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution

ProDrive：通过自我环境共演实现自动驾驶的前瞻性规划

Chuyao Fu, Shengzhe Gan, Zhuoli Ouyang, Yuhan Rui, Xiaowei Chi, Sirui Han, Jiankun Wang, Hong Zhang

机构 * Southern University of Science and Technology（南方科技大学） ； Hong Kong University of Science and Technology（香港科技大学） 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；BEV(abstract,abstract_cn)；分类 cs.RO

AI总结 ProDrive通过自我环境共演实现自动驾驶前瞻性规划，结合查询导向的轨迹规划器和鸟瞰图世界模型，提升安全性和规划效率。

Comments Accepted to CVPR 2026 GigaBrain Challenge Workshop

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning

风险地图作为中间件：迈向风险感知的可解释协作端到端自动驾驶

Mingyue Lei, Zewei Zhou, Hongchen Li, Jiaqi Ma, Jia Hu

机构 * Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University（教育部道路与交通工程重点实验室，同济大学） ； UCLA Mobility Lab, University of California, Los Angeles（加州大学洛杉矶分校UCLA移动实验室） 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；trajectory planning(abstract)；end-to-end driving(abstract)；分类 cs.RO

AI总结 本文提出RiskMM框架，通过风险地图中间件提升自动驾驶的可解释性和风险感知规划能力。

Comments IEEE RA-L

Real-Time Joint Simulation of LiDAR Perception and Motion Planning for Automated Driving

Zhanhong Huang, Xiao Zhang, Xinming Huang

专题命中 规划控制 ：LiDAR(title,abstract)；autonomous driving(abstract)；occupancy(abstract)；分类 cs.RO

IRL-DAL: Safe and Adaptive Trajectory Planning for Autonomous Driving via Energy-Guided Diffusion Models

IRL-DAL：通过能量引导扩散模型实现自动驾驶的安全自适应轨迹规划

Seyed Ahmad Hosseini Miangoleh, Amin Jalal Aghdasian, Farzaneh Abdollahi

机构 * Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic)（电气工程系，阿米尔卡比尔技术大学（德黑兰理工大学）） 纠错

专题命中 规划控制 ：autonomous driving(title)；trajectory planning(title)；分类 cs.RO、cs.AI

AI总结 本文提出IRL-DAL框架，通过能量引导扩散模型实现自动驾驶的安全自适应轨迹规划，结合模仿学习与强化学习，提升行驶安全性和鲁棒性。

VERDI: VLM-Embedded Reasoning for Autonomous Driving

VERDI：嵌入视觉语言模型的自动驾驶推理

Bowen Feng, Zhiting Mei, Julian Ost, Filippo Ghilotti, Baiang Li, Roger Girgis, Anirudha Majumdar, Felix Heide

机构 * Princeton University（普林斯顿大学） ； Torc Robotics 纠错

专题命中 规划控制 ：autonomous driving(title,abstract)；trajectory planning(abstract)；分类 cs.RO、cs.CV、cs.AI

AI总结 VERDI通过在训练时整合视觉语言模型的推理过程和常识知识，提升自动驾驶系统的决策能力，实现更高效的模块化架构，同时保持快速推理速度。