Automated Driving
Automated driving research aims to develop safe and reliable systems capable of navigating complex environments without human intervention. Current efforts focus on improving perception (using techniques like deep learning for high-definition map creation and amodal instance segmentation), decision-making (employing methods such as Monte Carlo tree search and model predictive control), and robust testing (leveraging virtual environments and small-scale testbeds to evaluate performance under various conditions, including failures). This field is significant due to its potential to revolutionize transportation, enhancing safety, efficiency, and accessibility, while also driving advancements in areas like computer vision, artificial intelligence, and robotics.
Papers
Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers
Yongqi Dong, Tobias Datema, Vincent Wassenaar, Joris van de Weg, Cahit Tolga Kopar, Harim Suleman
Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning
Henan Yuan, Penghui Li, Bart van Arem, Liujiang Kang, Yongqi Dong