Enhanced Perception for Safer Autonomous Driving: Roadside Multi-Modal Sensor Fusion and 3D Detection
#autonomous-driving
#autonomous-systems
#sensor-fusion
#safety
Enhanced Perception for Safer Autonomous Driving: Roadside Multi-Modal Sensor Fusion and 3D Detection
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This research addresses critical automotive challenges, including road safety and traffic congestion, by enhancing autonomous vehicle perception through roadside multi-modal sensor fusion. By combining data from onboard and roadside sensors, the study improves 3D object detection, particularly in complex urban environments. It also introduces new datasets for benchmarking and proposes advanced methods for real-time traffic participant detection and tracking, contributing to safer and more efficient autonomous driving systems.
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Dr. rer. nat. Walter Zimmer is a post-doctoral researcher at the University of California Los Angeles (UCLA). He received his Ph.D. from the Technical University of Munich (TUM) in 2025. His research focuses on cooperative autonomous driving, 3D perception and 3D foundation models. He has authored over 30 publications at top venues such as CVPR, ICCV, ECCV, ICML, NeurIPS, and T-PAMI. Dr. Zimmer previously worked as an Autonomous Systems Engineer at the STTech startup and holds a master’s degree from TUM. His work has earned multiple awards, including the IEEE ITSS Best Student Paper Award 2023 and Best Dissertation Award 2025.
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Co-sponsored by
Australian Centre for Robotics (The University of Sydney)
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Dr. rer. nat. Walter Zimmer
Dr. rer. nat. Walter Zimmer is a post-doctoral researcher at the University of California Los Angeles (UCLA). He received his Ph.D. from the Technical University of Munich (TUM) in 2025. His research focuses on cooperative autonomous driving, 3D perception and 3D foundation models. He has authored over 30 publications at top venues such as CVPR, ICCV, ECCV, ICML, NeurIPS, and T-PAMI. Dr. Zimmer previously worked as an Autonomous Systems Engineer at the STTech startup and holds a master’s degree from TUM. His work has earned multiple awards, including the IEEE ITSS Best Student Paper Award 2023 and Best Dissertation Award 2025.