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UM Ford Center for Autonomous Vehicles (FCAV)

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Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection
Predicting pedestrian movement in 3D for driverless cars
Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments
Pedx: Benchmark dataset for metric 3d pose estimation of pedestrians in complex urban intersections
Safe, Aggressive Quadrotor Flight via Reachability-based Trajectory Design
Provably Not-at-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments
Guaranteed Safe Reachability-based Trajectory Design for a high-fidelity, autonomous passenger vehicle
Attack Detection via Dynamic Watermarking
Rover Demo: Receding-Horizon Trajectory Design for Mobile Robots
Segway Demo: Receding-Horizon Trajectory Design for Mobile Robots
Failing to Learn: Autonomously Identifying Perception Failures for Self-Driving Cars
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