SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
Matt Vitelli, Yan Chang, Yawei Ye, Maciej Wołczyk, Błażej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, Peter Ondruska
Merging ML and expert planning
Hybrid ML planning system
System overview: SafetyNet combines a machine-learned planner with an effective rule-based fallback layer to deliver safe learned planning for SDVs.
Model architecture: Our ML planner model is built on GNNs consist of a PointNet-based local subgraph and a global graph using a Transformer encoder.
Model Training: Our model is trained using imitation learning on 300hr training data collected at Palo Alto and San Francisco.
Real world driving results
The results in this section show the SafetyNet controlled SDVs driving in downtown San Francisco.
Stopping for a Lead Vehicle
Stopping for Red Traffic Light
Adjusting Speed for a Vehicle Cut-In
Slowing for a Perpendicular Cut-In
Nudging around a Vehicle on the Left
Nudging around a Parklet on the Right
Nudging around a Truck on the Right
Safely Driving through a Yellow Light
Impact of SafetyNet
Safety events as a function of dataset size for ML planner with- and without fallback layer. We see that the addition of fallback layer significantly improves safety metrics.
Compared with ML planner-only approach, SafetyNet significantly reduces collisions, close calls, and discomfort braking, at the expense of slightly more passiveness.