Autonomous driving carries the promise to drastically reduce the number of car accidents; however, recently reported fatal crashes involving self-driving cars show that such an important goal is not yet achieved. This calls for better testing of the software controlling self-driving cars, which is difficult because it requires producing challenging driving scenarios. To better test self-driving car soft- ware, we propose to specifically test car crash scenarios, which are critical par excellence. Since real car crashes are difficult to test in field operation, we recreate them as physically accurate simulations in an environment that can be used for testing self-driving car software. To cope with the scarcity of sensory data collected during real car crashes which does not enable a full reproduction, we extract the information to recreate real car crashes from the police reports which document them. Our extensive evaluation, consisting of a user study involving 34 participants and a quantitative analysis of the quality of the generated tests, shows that we can generate accurate simulations of car crashes in a matter of minutes. Compared to tests which implement non critical driving scenarios, our tests effectively stressed the test subject in different ways and exposed several shortcomings in its implementation.

@inproceedings{paperbot5067,
  title = {Generating effective test cases for self-driving cars from police reports},
  author = {Alessio Gambi and Tri Huynh and Gordon Fraser},
  booktitle = {FSE},
  year = {2019},
  doi = {10.1145/3338906.3338942}
}