自动驾驶

Benchmarking the Robustness of Optical Flow Estimation to Corruptions

Zhonghua Yi, Hao Shi, Qi Jiang, Yao Gao, Ze Wang, Yufan Zhang, Kailun Yang, Kaiwei Wang

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、eess.IV

Comments The benchmarks and source code will be released at https://github.com/ZhonghuaYi/optical_flow_robustness_benchmark

Promptable Closed-loop Traffic Simulation

Shuhan Tan, Boris Ivanovic, Yuxiao Chen, Boyi Li, Xinshuo Weng, Yulong Cao, Philipp Krähenbühl, Marco Pavone

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Accepted to CoRL 2024. Website available at https://ariostgx.github.io/ProSim

SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries

Wei-Jer Chang, Francesco Pittaluga, Masayoshi Tomizuka, Wei Zhan, Manmohan Chandraker

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Accepted by ECCV2024; Project website: https://safe-sim.github.io/

Embodied Navigation at the Art Gallery

Roberto Bigazzi, Federico Landi, Silvia Cascianelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

专题命中 仿真评测 ：occupancy(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Accepted by 21st International Conference on Image Analysis and Processing (ICIAP 2021)

Data-driven Traffic Simulation: A Comprehensive Review

Di Chen, Meixin Zhu, Hao Yang, Xuesong Wang, Yinhai Wang

专题命中 仿真评测 ：autonomous driving(abstract)；driving perception(abstract)

Comments 21 pages, 7 figures, 6 tables

Learning to Drive Anywhere

Ruizhao Zhu, Peng Huang, Eshed Ohn-Bar, Venkatesh Saligrama

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Conference on Robot Learning (CoRL) 2023

Learning Off-Road Terrain Traversability with Self-Supervisions Only

Junwon Seo, Sungdae Sim, Inwook Shim

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Accepted to IEEE Robotics and Automation Letters. Our video can be found at https://bit.ly/3YdKanw

Journal ref IEEE Robotics and Automation Letters, 8.8 (2023):4617-4624

Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems

Fitash Ul Haq, Donghwan Shin, Lionel Briand

专题命中 仿真评测 ：autonomous driving(abstract)；end-to-end driving(abstract)

PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding

Hao Shi, Yifan Zhou, Kailun Yang, Xiaoting Yin, Ze Wang, Yaozu Ye, Zhe Yin, Shi Meng, Peng Li, Kaiwei Wang

专题命中 仿真评测 ：self-driving(abstract)；分类 cs.RO、cs.CV、eess.IV

Comments Code and dataset are publicly available at https://github.com/MasterHow/PanoFlow

Unifying Evaluation of Machine Learning Safety Monitors

Joris Guerin, Raul Sena Ferreira, Kevin Delmas, Jérémie Guiochet

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments 9 pages, 5 figures, 3 tables, to appear in the proceedings of the 33rd IEEE International Symposium on Software Reliability Engineering (ISSRE 2022)

LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane

Ze Wang, Kailun Yang, Hao Shi, Peng Li, Fei Gao, Kaiwei Wang

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、eess.IV

Comments Accepted to IROS 2022. Dataset and code are publicly available at https://github.com/flysoaryun/LF-VIO

Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing

Jonathan Francis, Bingqing Chen, Siddha Ganju, Sidharth Kathpal, Jyotish Poonganam, Ayush Shivani, Vrushank Vyas, Sahika Genc, Ivan Zhukov, Max Kumskoy, Anirudh Koul, Jean Oh, Eric Nyberg

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments 20 pages, 4 figures, 2 tables

SelfD: Self-Learning Large-Scale Driving Policies From the Web

Jimuyang Zhang, Ruizhao Zhu, Eshed Ohn-Bar

专题命中 仿真评测 ：BEV(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Accepted by CVPR2022

Pose Estimation of Specific Rigid Objects

Tomas Hodan

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Tomas Hodan's PhD thesis defended on July 7, 2021. Supervisor: Prof. Jiri Matas. Reviewers: Prof. Vincent Lepetit, Prof. Markus Vincze, Dr. Slobodan Ilic. A recording of the defense: https://youtu.be/WAQmubEXCRM

Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping

Jean-Emmanuel Deschaud, David Duque, Jean Pierre Richa, Santiago Velasco-Forero, Beatriz Marcotegui, and François Goulette

专题命中 仿真评测 ：LiDAR(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments 24 pages

Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients

Oliver Scheel, Luca Bergamini, Maciej Wołczyk, Błażej Osiński, Peter Ondruska

专题命中 仿真评测 ：self-driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments CoRL 2021

iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes

Bokui Shen, Fei Xia, Chengshu Li, Roberto Martín-Martín, Linxi Fan, Guanzhi Wang, Claudia Pérez-D'Arpino, Shyamal Buch, Sanjana Srivastava, Lyne P. Tchapmi, Micael E. Tchapmi, Kent Vainio, Josiah Wong, Li Fei-Fei, Silvio Savarese

专题命中 仿真评测 ：LiDAR(abstract)；分类 cs.RO、cs.CV、cs.AI

Journal ref 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)

Evaluation Framework for Performance Limitation of Autonomous Systems under Sensor Attack

Koichi Shimizu, Daisuke Suzuki, Ryo Muramatsu, Hisashi Mori, Tomoyuki Nagatsuka, Tsutomu Matsumoto

专题命中 仿真评测 ：self-driving(abstract)；LiDAR(abstract)

TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

Simon Suo, Sebastian Regalado, Sergio Casas, Raquel Urtasun

专题命中 仿真评测 ：self-driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Learning by Cheating

Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Paper published in CoRL2019

Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots

Jianzhu Huai, Yusen Qin, Fumin Pang, Zichong Chen

专题命中 仿真评测 ：LiDAR(abstract)；分类 cs.RO、cs.CV、eess.IV

Comments 8 pages, 4 figures, 4 tables, conference

CARLA: An Open Urban Driving Simulator

Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, Vladlen Koltun

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Published at the 1st Conference on Robot Learning (CoRL)

CarLLaVA: Vision language models for camera-only closed-loop driving

Katrin Renz, Long Chen, Ana-Maria Marcu, Jan Hünermann, Benoit Hanotte, Alice Karnsund, Jamie Shotton, Elahe Arani, Oleg Sinavski

专题命中 仿真评测 ：autonomous driving(abstract,comments)；分类 cs.RO、cs.CV

Comments Outstanding Champion & Innovation Award @ CARLA Autonomous Driving Challenge 2024; Project video: https://youtu.be/E1nsEgcHRuc

RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos

RealityBridge: 连接可编辑3D高斯泼溅驾驶模拟与现实世界视频

Zhenhua Wu, Yun Pang, Mingkun Chang, Yuwei Ning, Liangzhi Wang, Yi Xiao, Guanbin Li

机构 * Sun Yat-sen University（中山大学） ； Guangdong Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室） ； Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室） 纠错

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

AI总结 提出RealityBridge框架，利用多模态控制和轻量级GateNet，结合自回归长视频训练与奖励引导后训练，缩小编辑后3DGS驾驶视频的Sim-to-Real差距，提升视觉真实感和时间一致性。

Comments Under submission

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

TerraZero：用于大规模零演示自博弈的过程式驾驶模拟

Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang, Zachary Lee, Saeed Bonab, Tim Wang, Wei Zhan

机构 * Applied Intuition（应用直觉） 纠错

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

AI总结 研究旨在训练强大自动驾驶智能体，提出TerraZero过程式驾驶模拟器和自博弈训练堆栈，其速度快、逼真且多样。通过特定方式填充地图生成无限场景，智能体仅靠强化学习训练，能零样本泛化，在多个基准测试中表现出色。

Comments Technical Report from Applied Intuition Research

DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks

DSBench：用于评估外部和车内风险的综合基准测试

Xianhui Meng, Yuchen Zhang, Zhijian Huang, Zheng Lu, Ziling Ji, Yandan Lin, Yaoyao Yin, Hongyuan Zhang, Wei Zhou, Guangfeng Jiang, Li Zhang, Long Chen, Hangjun Ye, Jun Liu, Xiaoshuai Hao

机构 * University of Science and Technology of China（中国科学技术大学） ； Georgia Institute of Technology（佐治亚理工学院） ； Xiaomi EV（小米电动车） ； Fudan University（复旦大学） ； Xidian University（西安电子科技大学） ； South China University of Technology（华南理工大学） ； The University of Hong Kong（香港大学） 纠错

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

AI总结 针对视觉语言模型在安全关键场景适用性未充分探索的问题，引入DSBench综合基准测试，涵盖外部与车内风险，分多类别。评估发现模型在复杂情况下降，构建数据集微调可提升性能，推动自动驾驶技术发展，相关资源将公开。

Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving

以5Hz思考，20Hz行动：用于闭环驾驶的异步快慢视觉-语言-动作推理

Yun Li, Jiachen Gong, Simon Thompson, Ehsan Javanmardi, Qunli Zhang, Zifan Zeng, Shiming Liu, Peng Wang, Zixuan Guo, Manabu Tsukada

机构 * The University of Tokyo（东京大学） ； TIER IV, Inc.（TIER IV公司） ； Huawei（华为） 纠错

专题命中 仿真评测 ：end-to-end driving(abstract)；分类 cs.RO、cs.AI

AI总结 研究针对大语言模型用于闭环驾驶时推理延迟与车辆控制速率冲突的问题，提出快慢架构，慢系统用冻结主干处理历史，快专家基于缓存和当前帧回归航路点，经训练在CARLA上提升路线完成率，降低误差且可零样本转移。

Comments 13 pages, 5 figures, 4 tables

Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

即时NuRec：用于驾驶场景模拟的前馈3D高斯重建

NVIDIA, :, Jiahui Huang, Jiawei Ren, Michal Tyszkiewicz, Bjoern Haefner, Michael Shelley, Xin Kang, Seung Wook Kim, Ning Xu, Qi Wu, Janick Martinez Esturo, Shengyu Huang, Nick Schneider, Laura Leal-Taixe, Zan Gojcic, Sanja Fidler

机构 * NVIDIA 纠错

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.CV、cs.AI

AI总结 针对自动驾驶3D模拟平台中神经模拟方法存在的速度慢和需逐场景调整问题，提出即时NuRec这一前馈神经重建模型，可快速将多视图驾驶日志转为3DGS世界，在数据集上PSNR表现出色，还能用于闭环模拟。

Comments Project Page: https://research.nvidia.com/labs/sil/projects/instant-nurec/

Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators

在信任世界模型模拟器的裁决之前先验证其可靠性：世界模型模拟器的可接受性

Christian Oefinger, Finn Rasmus Schäfer, Korbinian Moller, Mattia Piccinini, Johannes Betz

机构 * Autonomous Vehicle Systems Lab（自主车辆系统实验室） ； Technical University of Munich（慕尼黑技术大学） 纠错

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.AI

AI总结 研究世界模型模拟器裁决的可靠性问题，基于安全关键模拟的可信度实践定义可接受性阶梯，通过自动驾驶实例验证，指出视觉保真度不能预测行动稳健性，为世界模型模拟器的评估提供了新框架。

Comments Accepted at RSS 2026 Workshop on Robot World Models

Towards Metric-Agnostic Trajectory Forecasting

迈向度量无关的轨迹预测

Markus Knoche, Daan de Geus, Bastian Leibe

机构 * RWTH Aachen University（亚琛工业大学） ； Eindhoven University of Technology（埃因霍温理工大学） 纠错

专题命中 仿真评测 ：autonomous driving(abstract)；分类 cs.RO、cs.CV

AI总结 提出度量无关的概率训练目标，并引入TraDiE策略将预测分布映射为轨迹和置信度，实现度量优化作为下游任务，在Waymo基准上取得最优结果。

Comments ECCV 2026. Project page at https://vision.rwth-aachen.de/TraDiE-policies