An end-to-end planner that jointly learns prediction, lane-selection decisions, and trajectory optimization with a differentiable optimizer reports lower collision rates and higher progress than imitation-based baselines on Waymo.
Autonomous Driving: A Bird’s Eye View,
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Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
An end-to-end planner that jointly learns prediction, lane-selection decisions, and trajectory optimization with a differentiable optimizer reports lower collision rates and higher progress than imitation-based baselines on Waymo.