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.

本论文提出了一种新方法，通过模拟自动驾驶汽车感知和预测系统的输出，以可靠地进行运动规划测试，从而取代传统的昂贵且具有较大领域差距的传感器模拟。我们使用真实感知和预测输出与标签数据进行训练，从而预测在线系统的输出。此外，我们的方法以高清地图、边界框和轨迹为输入，可由测试工程师轻松手绘几分钟，这大大提高了可扩展性。通过两个大型数据集的定量实验结果表明，我们可以实现通过模拟来进行现实测试的全面方法。