What data do we need for training an AV motion planner?
Long Chen, Lukas Platinsky, Stefanie Speichert, Błażej Osiński, Oliver Scheel, Yawei Ye, Hugo Grimmett, Luca del Pero, and Peter Ondruska
Data are key for ML planning
Collecting large-scale ML planning datasets using fleet
Balancing the trade-offs we can collect large-scale datasets using low-cost sensors to train ML planning systems deployed on SDVs with expensive high-end sensors.
Finding the optimal hardware configuration
We developed a pipeline to simulate lower-quality data from low-cost sensors, and evaluate the ML planner performance, in order to find the optimal sensor configuration
Simulated lower-quality data is create, by limiting the range and FoV, or changing the perception accuracy by adding random noises to the agent positions
Impact on ML Planner Performance
Sensitivity Analysis on Data Quality
ML Planner needs at least 40m range, 130 FoV to have acceptable performance, and is not sensitive to rotational error.
Quality vs Quantity
With domain adaptation, greater amount of lower quality data beat small amount of high-quality data