Automatic Generation of Test-cases of Increasing Complexity for Autonomous Vehicles at Intersections

Abstract

This paper presents a new framework for generating test-case scenarios for autonomous vehicles. We address two challenges in automatic test-case generation: first, a formal notion of test-case complexity, and second, an algorithm to generate more-complex test-cases. We characterize the complexity of a test-case by its set of solutions, and compare two complexities by the subset relation. The novelty of our definition is that it only relies on the pass-fail criteria of the test-case, rather than indirect or subjective assessments of what may challenge an ego vehicle to pass a test-case. Given a test-case, we model the problem of generating a more complex test-case as a constraint-satisfaction search problem. The search variables are the changes to the given test-case, and the search constraints define a solution to the search problem. The constraints include steering geometry of cars, the geometry of lanes, the shape of cars, traffic rules, bounds on longitudinal acceleration of cars, etc. To conquer the computational challenge, we divide the constraints to three cat-egories and satisfy them with simulation, answer set programming, and SMT solving. We have implemented our algorithm using the Scenic libraries and the CARLA simulator and generate test-cases for several 3-way and 4-way intersections with different topologies. Our experiments demonstrate that both CARLA's autopilot and autopilot-plus-RSS (Responsibility-Sensitive Safety) can fail as the complexity of test-cases increase.