On Traffic Situation Predictions for Automated Driving of Long Vehicle Combinations

Abstract: The introduction of longer vehicle combinations for road transports than are currently allowed is an important viable option for achieving the environmental goals on transported goods in Sweden and Europe by the year 2030. This thesis addresses how driver assistance functionality for high-speed manoeuvring can be designed and realized for prospective long vehicle combinations. The main focus is the derivation and usage of traffic situation predictions in order to provide driver support functionalities with a high driver acceptance. The traffic situation predictions are of a tactical character and include a time horizon of up to 10 s. Data collection of manual and automated driving with an A-double combination was carried out in a moving-base driving simulator. The driving scenario was comprised of a relatively curvy and hilly single-lane Swedish county road (180). The driving trajectories were analysed and complemented with results from optimization. Based on observations of utilized accelerations it was proposed that the combined steering and braking should prioritize a smooth and comfortable driving experience. It was hypothesized that high driver acceptance of driver assistance functionality including automated steering and propulsion/braking, can be realized by utilizing driver models inspired by human cognition as an integrated part in the generation of traffic situation predictions. A longitudinal and lateral driver model based on optic information was proposed for lane-change manoeuvring. The driver model was implemented in a real-time framework for automated driving of an A-double combination on a multiple lane one-way road. Simulations showed that the framework gave reasonable results for maintain lane and lane change manoeuvres at constant and varying longitudinal velocities.