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The autonomous driving industry is continuously dealing with moresafety-critical scenarios, and nonlinear model predictive control (NMPC) is apowerful control strategy for handling such situations. However, standardsafety constraints are not scalable and require a long NMPC horizon. Moreover,the adoption of NMPC in the automotive industry is limited by the heavycomputation of numerical optimization routines. To address those issues, thispaper presents a real-time capable NMPC for automated driving in urbanenvironments, using control barrier functions (CBFs). Furthermore, the designedNMPC is based on a novel collocation transcription approach, named RESAFE/COL,that allows to reduce the number of optimization variables while stillguaranteeing the continuous time (nonlinear) inequality constraintssatisfaction, through regional convex hull approximation. RESAFE/COL is provento be 5 times faster than multiple shooting and more tractable for embeddedhardware without a decrease in the performance, nor accuracy and safety of thenumerical solution. We validate our NMPC-CBF with RESAFE/COL approach withhighly accurate digital twins of the vehicle and the urban environment and showthe safe controller's ability to improve crash avoidance by 91%.

Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation