Autonomous Driving
Autonomous driving research aims to develop vehicles capable of navigating and operating without human intervention, prioritizing safety and efficiency. Current efforts heavily focus on improving perception (using techniques like 3D Gaussian splatting and Bird's-Eye-View representations), prediction (leveraging diffusion models, transformers, and Bayesian games to handle uncertainty), and planning (employing reinforcement learning, large language models, and hierarchical approaches for decision-making). These advancements are crucial for enhancing the reliability and safety of autonomous vehicles, with significant implications for transportation systems and the broader AI community.
Papers
GAS: Generating Fast and Accurate Surrogate Models for Autonomous Vehicle Systems
Keyur Joshi, Chiao Hsieh, Sayan Mitra, Sasa Misailovic
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor
Alberto Viale, Alberto Marchisio, Maurizio Martina, Guido Masera, Muhammad Shafique
MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing
Phillip Karle, Ferenc Török, Maximilian Geisslinger, Markus Lienkamp
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability
Senay Cakir, Marcel Gauß, Kai Häppeler, Yassine Ounajjar, Fabian Heinle, Reiner Marchthaler
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
Hui-Xian Cheng, Xian-Feng Han, Guo-Qiang Xiao