Code — UM Ford Center for Autonomous Vehicles (FCAV)

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Low Latency Trajectory Predictions for Interaction Aware Highway Driving

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Off The Beaten Sidewalk: Pedestrian Prediction In Shared Spaces For Autonomous Vehicles

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Pixel-Wise Motion Deblurring of Thermal Videos

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Unsupervised Pedestrian Pose Prediction: A Deep Predictive Coding Network-Based Approach for Autonomous Vehicle Perception

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Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection Based on Planar LiDAR and Monocular Images

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Stochastic Sampling Simulation for Pedestrian Trajectory Prediction

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Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars

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Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation

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RTD (Reachability-based Trajectory Design)

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Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

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