Traditional lane-changing (LC) behavioral researches usually focus on the
driver?s cognitive performance which includes the driver?s psychological and
behavioral habit characteristics, rarely involving the affection of expert
driver?s comprehensive behavioral preferences, such as: safety and comfort
performance in LC process. Towards the free LC process, a novel LC safety and
comfort degree index is proposed in this paper, as well as, the novel
definition of LC driving behavioral preferences is described in detail.
Taking advantage of interactive evolutionary computing (IEC) and real-time
optimization (RTO) metrics, a kind of LC behavioral preferences on-line
learning agent extending traditional Belief-Desire-Intention (BDI) structure
is explicitly proposed, which can perform behavioral preferences learning
activities in the LC process. In addition, driving behavioral preferences
learning strategies are introduced which can gradually grasp essentials in
driver?s subjective judgments in decision-making of the LC process and make
the LC process more safety and scientific. Specifically, a conceptual model
of the agent, driving behavioral preferences learning-BDI (DpL-BDI) agent is
introduced, along with corresponding functional modules to grasp driving
behavioral preferences. Furthermore, colored Petri nets are used to realize
the components and scheduler of the DpL-BDI agents. In the end, to compare
with the traditional LC parameters? learning methods (such as: the least
squares methods and Genetic Algorithms), a kind of LC problems is suggested
to case studies, testing and verifying the validity of the contribution.
A lane-changing behavioral preferences learning agent with its applications
