Autonomous Vehicle
Autonomous vehicles (AVs) aim to achieve safe and efficient self-driven navigation, primarily focusing on robust perception, decision-making, and control in complex and unpredictable environments. Current research emphasizes improving perception through advanced sensor fusion (e.g., LiDAR, cameras, radar) and data processing techniques like deep learning and computer vision, coupled with sophisticated planning algorithms (e.g., Markov Decision Processes, behavior trees, and game theory) for safe and efficient trajectory generation. This field is significant for its potential to revolutionize transportation, enhancing safety, efficiency, and accessibility, while also driving advancements in artificial intelligence, robotics, and control systems.
Papers
Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning
Robert Kaufman, Emi Lee, Manas Satish Bedmutha, David Kirsh, Nadir Weibel
Direct-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention
Yihang Tao, Senkang Hu, Zhengru Fang, Yuguang Fang
Are Existing Road Design Guidelines Suitable for Autonomous Vehicles?
Yang Sun, Christopher M. Poskitt, Jun Sun
Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles
Yiming Shu, Jingyuan Zhou, Fu Zhang
TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis
Nikolai Polley, Svetlana Pavlitska, Yacin Boualili, Patrick Rohrbeck, Paul Stiller, Ashok Kumar Bangaru, J. Marius Zöllner
Towards Using Active Learning Methods for Human-Seat Interactions To Generate Realistic Occupant Motion
Niklas Fahse, Monika Harant, Marius Obentheuer, Joachim Linn, Jörg Fehr
Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections
Qing Li, Jinxing Hua, Qiuxia Sun
MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science
Mahdieh Aliazam, Ali Javadi, Amir Mahdi Hosseini Monazzah, Ahmad Akbari Azirani
Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles
Qiujing Lu, Xuanhan Wang, Yiwei Jiang, Guangming Zhao, Mingyue Ma, Shuo Feng
Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception
Lei He, Qiaoyi Wang, Honglin Sun, Qing Xu, Bolin Gao, Shengbo Eben Li, Jianqiang Wang, Keqiang Li
What Did My Car Say? Autonomous Vehicle Explanation Errors, Context, and Personal Traits Impact Comfort, Reliance, Satisfaction, and Driving Confidence
Robert Kaufman, Aaron Broukhim, David Kirsh, Nadir Weibel
Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priors
Jiaqi Liu, Peng Hang, Xiaoxiang Na, Chao Huang, Jian Sun
Driving with Prior Maps: Unified Vector Prior Encoding for Autonomous Vehicle Mapping
Shuang Zeng, Xinyuan Chang, Xinran Liu, Zheng Pan, Xing Wei
Achieving the Safety and Security of the End-to-End AV Pipeline
Noah T. Curran, Minkyoung Cho, Ryan Feng, Liangkai Liu, Brian Jay Tang, Pedram MohajerAnsari, Alkim Domeke, Mert D. Pesé, Kang G. Shin
Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Autonomous Vehicles
Miao Fan, Yi Yao, Jianping Zhang, Xiangbo Song, Daihui Wu
CONClave -- Secure and Robust Cooperative Perception for CAVs Using Authenticated Consensus and Trust Scoring
Edward Andert, Francis Mendoza, Hans Walter Behrens, Aviral Shrivastava
Want a Ride? Attitudes Towards Autonomous Driving and Behavior in Autonomous Vehicles
Enrico Del Re, Leonie Sauer, Marco Polli, Cristina Olaverri-Monreal