Sequential Convex Programming Methods for Real-time Trajectory Optimization in Autonomous Vehicle Racing

We present a real-time-capable Model Predictive Controller (MPC) based on a single-track vehicle model and Pacejka’s magic tire formula for autonomous racing applications. After formulating the general non-convex trajectory optimization problem, the model is linearized around estimated operating points and the constraints are convexified using the Sequen- tial Convex Programming (SCP) method. We use two different methods to convexify the non-convex track constraints, namely Sequential Linearization (SL) and Sequential Convex Restriction (SCR). SL, a method of relaxing the constraints, was introduced in our previous paper. SCR, a method of restricting the con- straints, is introduced in this paper. We show the application of SCR to autonomous racing and prove that it does not interfere with recursive feasibility. We compare the predicted trajectory quality for the nonlinear single-track model to the linear double integrator model from our previous paper. The MPC performance is evaluated on a scaled version of the Hockenheimring racing track. We show that an MPC with SCR yields faster lap times than an MPC with SL – for race starts as well as flying laps – while still being real-time capable. A video showing the results is available at https://youtu.be/21iETsolCNQ.