Predictive control for autonomous driving with experimental evaluation on a heavy-duty construction truck

Autonomous vehicles is a rapidly expanding field, and promise to play an important role in society. The goal of autonomous vehicles is to improve the traffic system in terms of higher efficiency, fewer accidents, lower fuel consumption, less pollution, and shorter travel times. In more isolated environments, such as mines and ports, vehicle automation can bring significant efficiency and production benefits and it eliminates repetitive jobs that can lead to inattention and accidents. The results of this thesis are developed within the iQMatic project, which is lead by Scania CV AB in collaboration with Saab AB, Autoliv, Linköping University, and KTH, with the goal of developing a fully autonomous truck for mining applications by 2018. The thesis addresses the problem of lateral and longitudinal dynamics control of autonomous ground vehicles with the purpose of accurate and smooth path following. Clothoids are curves with linearly varying curvature, which are widely used in road design due to their smoothness properties. In the thesis, clothoids are used in the design of optimal predictive controllers aimed at minimizing the lateral forces and jerks in the vehicle. First, a clothoid-based path sparsification algorithm is proposed to efficiently describe the reference path. This approach relies on a sparseness regularization technique such that a minimal number of clothoids is used to describe the reference path. Second, a clothoid-based model predictive controller (MPCC) is proposed. This controller aims at producing a smooth driving by taking advantage of the clothoid properties. The motion of a vehicle traveling at low speeds is assumed to define a clothoid segment if the steering angle is chosen to vary piecewise linearly with respect to the traveled distance. Third, an alternative formulation of a controller for smooth driving is presented. In this case, we formulate the problem as an economic model predictive controller (EMPC). In EMPC the objective function contains an economic cost (here represented by comfort or smoothness), which is described by the second and first derivatives of the curvature. This results in that the obtained curvature is approximately linear. Fourth, the generation of feasible speed profiles, and the longitudinal vehicle control for following these, is studied. The speed profile generation is formulated as an optimization problem with two contradictory objectives: to drive as fast as possible (close to the speed limit) while accelerating as little as possible. Constraints are given by vehicle limitations and road geometry. The longitudinal controller is formulated in a similar way, but is implemented in a receding horizon fashion, and provides feasible reference speeds to a cruise controller. Finally, experimental and simulation evaluation are performed. The EMPC is deployed on a Scania construction truck. The experimental evaluation with the EMPC demonstrates its good performance, since the deviation from the path never exceeds 30 cm and in average is 6 cm. A simulation environment is developed allowing evaluation and validation of the control design before deploying it in the experimental platform. The EMPC and the MPCC are compared with a pure-pursuit controller (PPC) and a standard MPC. The results demonstrate the effectiveness of both the MPCC and the EMPC. Furthermore, the EMPC clearly outperforms the PPC in terms of path accuracy and the standard MPC in terms of driving smoothness.