Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
This provides a scalable safety testing method for self-driving vehicles, though it is incremental as it builds on existing simulation approaches.
The paper tackles the problem of testing self-driving vehicle safety by simulating perception and prediction outputs instead of sensors, enabling realistic motion planning testing with scalable inputs like maps and trajectories that can be sketched in minutes.
We present a novel method for testing the safety of self-driving vehicles in simulation. We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. Instead, we directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing. Specifically, we use paired data in the form of ground truth labels and real perception and prediction outputs to train a model that predicts what the online system will produce. Importantly, the inputs to our system consists of high definition maps, bounding boxes, and trajectories, which can be easily sketched by a test engineer in a matter of minutes. This makes our approach a much more scalable solution. Quantitative results on two large-scale datasets demonstrate that we can realistically test motion planning using our simulations.