A prediction-based reactive driving strategy for highly automated driving function on freeways

Highly automated driving on freeways requires a complex artificial intelligence that makes optimal decisions based on the current measurements and information. The architecture of the decision-making process, hereinafter referred to as driving strategy, should allow diversity in decision-making for various traffic situations and modular expandability of the overall intelligence. Besides a reactive response to changes in the dynamic environment, a deliberative component should also be considered to incorporate the future evolution of the environment. This paper presents a novel driving strategy that meets the above requirements. The complex driving task is discretized by organization into a finite set of “behavioral strategies” through the developed “decision network”. The decision-making process itself is realized by a nonlinear model predictive approach which is solved using combinatorial optimization formulation. Lastly, the capability of the proposed approach is demonstrated in two freeway situations.