DENALI is the first large-scale real-world dataset of space-time histograms from low-cost LiDARs for training models to perceive hidden objects via multi-bounce light cues.
Multi-modal sensor fusion for auto driving perception: A survey
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Presents an AV Resilient architecture with redundancy, diversity, adaptive reconfiguration, and anomaly- and hash-based intrusion detection, experimentally validated on the Quanser QCar platform for detecting depth camera blinding and perception module tampering.
A survey organizes synthetic data use, digital twin simulation, and domain adaptation techniques for autonomous driving while identifying open challenges like Sim2Real transfer.
citing papers explorer
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DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs
DENALI is the first large-scale real-world dataset of space-time histograms from low-cost LiDARs for training models to perceive hidden objects via multi-bounce light cues.
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Security and Resilience in Autonomous Vehicles: A Proactive Design Approach
Presents an AV Resilient architecture with redundancy, diversity, adaptive reconfiguration, and anomaly- and hash-based intrusion detection, experimentally validated on the Quanser QCar platform for detecting depth camera blinding and perception module tampering.
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From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving
A survey organizes synthetic data use, digital twin simulation, and domain adaptation techniques for autonomous driving while identifying open challenges like Sim2Real transfer.