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
Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function
Haohao Hu, Hexing Yang, Jian Wu, Xiao Lei, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller
TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis
Haohao Hu, Fengze Han, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller