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
eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Driving
Pengfei Lin, Ehsan Javanmardi, Yuze Jiang, Dou Hu, Shangkai Zhang, Manabu Tsukada
Local map Construction Methods with SD map: A Novel Survey
Jiaqi Li, Pingfan Jia, Jiaxing Chen, Jiaxi Liu, Lei He
An Investigation of Denial of Service Attacks on Autonomous Driving Software and Hardware in Operation
Tillmann Stübler, Andrea Amodei, Domenico Capriglione, Giuseppe Tomasso, Nicolas Bonnotte, Shawan Mohammed
Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving
Gemb Kaljavesi, Xiyan Su, Frank Diermeyer
ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models
Shounak Sural, Naren, Ragunathan Rajkumar
Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations
Ahmed Hammam, Bharathwaj Krishnaswami Sreedhar, Nura Kawa, Tim Patzelt, Oliver De Candido
How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception
Mert Keser, Youssef Shoeb, Alois Knoll
HEAD: A Bandwidth-Efficient Cooperative Perception Approach for Heterogeneous Connected and Autonomous Vehicles
Deyuan Qu, Qi Chen, Yongqi Zhu, Yihao Zhu, Sergei S. Avedisov, Song Fu, Qing Yang
Fast and Modular Autonomy Software for Autonomous Racing Vehicles
Andrew Saba, Aderotimi Adetunji, Adam Johnson, Aadi Kothari, Matthew Sivaprakasam, Joshua Spisak, Prem Bharatia, Arjun Chauhan, Brendan Duff, Noah Gasparro, Charles King, Ryan Larkin, Brian Mao, Micah Nye, Anjali Parashar, Joseph Attias, Aurimas Balciunas, Austin Brown, Chris Chang, Ming Gao, Cindy Heredia, Andrew Keats, Jose Lavariega, William Muckelroy, Andre Slavescu, Nickolas Stathas, Nayana Suvarna, Chuan Tian Zhang, Sebastian Scherer, Deva Ramanan
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Yousef Emami, Luis Almeida, Kai Li, Wei Ni, Zhu Han
A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems
Shuo Yang, Liwen Wang, Yanjun Huang, Hong Chen