Abstract
This paper discusses cooperative path-planning and tracking controller for autonomous vehicles using a distributed model predictive control approach. Mixed-integer quadratic programming approach is used for optimal trajectory generation using a linear model predictive control for path-tracking. Cooperative behaviour is introduced by broadcasting the planned trajectories of two connected automated vehicles. The controller generates steering and torque inputs. The steering and drive motor actuator constraints are incorporated in the control law. Computational simulations are performed to evaluate the controller for vehicle models of varying complexities. A 12-degrees-of-freedom vehicle model is developed and is subsequently linearised to be used as the plant model for the linearised model predictive control-based tracking controller. The model behaviour is compared against the kinematic, bicycle and the sophisticated high-fidelity multi-body dynamics CarSim model of the vehicle. Vehicle trajectories used for tracking are longitudinal and lateral positions, velocities and yaw rate. A cooperative obstacle avoidance manoeuvre is performed at different speeds using a co-simulation between the controller model in Simulink and the high-fidelity vehicle model in CarSim. The simulation results demonstrate the effectiveness of the proposed method.
Highlights
The main components of an autonomous vehicle are perception, planning and control
The co-simulation study was performed using the path-planning and tracking controller model and the nonlinear complex vehicle dynamics model built in CarSim
The models have been compared for a single lane change manoeuvre and it has been established that the 12-dof model emulates the real vehicle dynamic characteristics fairly well
Summary
The main components of an autonomous vehicle are perception, planning and control. This paper discusses the planning and control aspects. Significant research has been done in path-planning strategies using mixed-integer quadratic programming (MIQP),[3] polynomials,[4] B-splines,[5] elastic bands[6] and potential fields.[7] In the research community, the path-planning problem has been widely studied for a single vehicle while in an autonomous driving environment multiple vehicles are present. A challenging research task is to achieve coordination by considering the trajectories of other autonomous vehicles. Schouwenaars et al.[8] used an optimal pathplanning approach for multiple vehicles based on MIQP. Frese and Beyerer[9] compared several cooperative path-planning algorithms like tree search, elastic bands, priority-based approach, etc. Frese and Beyerer[9] compared several cooperative path-planning algorithms like tree search, elastic bands, priority-based approach, etc. for their computational times
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