Driver Model Based Automated Driving of Long Vehicle Combinations in Emulated Highway Traffic

Abstract

This paper proposes a framework for automated highway driving of an A-double long vehicle combination. The included driving manoeuvres are maintain lane, lane change to right and left, abort lane change to right and left, and emergency brake. A combined longitudinal and lateral driver model is used for the generation of longitudinal acceleration and steering requests. The behaviour of the driver model, both regarding heuristics and safety thresholds, is inspired by human cognition and optical flow theory. Traffic situation predictions of feasible lane changes are calculated using the driver model in combination with prediction models of the subject and surrounding vehicles. The traffic situation predictions are used for the evaluation of constraints related to vehicle dynamics, road boundaries and distance to surrounding objects. When the framework is started, the subject vehicle is initiated in the maintain lane state respecting the road speed limit and the distance to surrounding objects. A lane change manoeuvre is performed on request from the driver when the corresponding traffic situation prediction and control request become feasible. The framework has been implemented in a simulation environment including a high-fidelity vehicle plant model and models of surrounding vehicles. Simulations show that the framework gives anticipated results when initial conditions are varied. Results are shown for maintain lane and lane change manoeuvres at constant longitudinal velocity, varying from 20-80 km/h and lane changes combined with retardation including leading vehicle braking from different initial velocities ranging from 30-80 km/h.