Solving Constraint Systems from Traffic Scenarios for the Validation of Autonomous Driving

The degree of automation in our daily life will grow rapidly. This leads to big challenges regarding the safety validation of autonomous robots which take over more and more tasks being – as of yet – predestinated for humans. This is in particular true for the emerging area of autonomous driving which aims at making road traffic safer, more efficient, more economic, and more comfortable. One promising approach for the safety validation of autonomous driving is the virtual simulation of traffic scenarios, i.e. conducting the majority of tests in virtual reality instead of the real world. In addition to quantity, the quality of such tests with a focus on critical traffic scenarios will be an essential ingredient for safety validation. In this paper, we investigate the concretization of traffic scenarios – in particular, scenarios which are specified as a set of constraints with interval parameters. We rely on the iSAT algorithm to perform the core reasoning, adapt its decision heuristics and complement it with an ICP-aware formula generation for non-linear formulas. The resulting Scenario Concretization Solver (SCS) – although being written in Java – is able to outperform SMT solvers on this problem class.