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Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.