Assistive tactical decisions for safe and fast trajectories

It is the dawn of an area where Advanced Driving Assistance Systems (ADAS) are gradually enhanced to provide fully automated systems, ADAS have huge potential for improving road safety and travel times. However their take-up in the market is very slow. Assistive systems should take into account driver’s preferences in terms of driving style in order to increase adoption rates. The aim of this paper is to compute online optimal trajectories given a traffic condition on a highway while considering the motorist’s driving style. Travel duration and safety are the main parameters used to find the optimal trajectory. A simulation framework to determine the optimal trajectory was developed in which the ego car travels in a highway environment scenario. An agent-oriented algorithm - using time and safety as optimality criteria – was defined for real-time feedback. The performance of the algorithm was compared against optimal trajectories computed offline with the hybrid A* algorithm. The new framework provides trajectories close to the optimal trajectory and is computationally achievable. The agents were shown to follow safe and fast trajectories in three tests scenarios: emergency braking, overtaking, and a complex situation with multiple vehicles around the ego vehicle.