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We develop a real-time feasible mixed-integer programming-based decisionmaking (MIP-DM) system for automated driving. Using a linear vehicle model in aroad-aligned coordinate frame, the lane change constraints, collision avoidanceand traffic rules can be formulated as mixed-integer inequalities, resulting ina mixed-integer quadratic program (MIQP). The proposed MIP-DM simultaneouslyperforms maneuver selection and trajectory generation by solving the MIQP ateach sampling time instant. While solving MIQPs in real time has beenconsidered intractable in the past, we show that our recently developed solverBB-ASIPM is capable of solving MIP-DM problems on embedded hardware in realtime. The performance of this approach is illustrated in simulations in variousscenarios including merging points and traffic intersections, andhardware-in-the-loop simulations on dSPACE Scalexio and MicroAutoBox-III.Finally, we present results from hardware experiments on small-scale automatedvehicles.

Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning