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Virtual scenario-based testing has become an acceptable method for evaluating safety effectiveness of advanced driver assistance systems (ADAS). Due to the complexity of the ADAS operating environment, the scenarios that an ADAS could face are almost infinite. Therefore, it is crucial to find critical scenarios to improve the efficiency of testing without compromising credibility. One popular method is to explore the parameterized scenario space using various intelligent search methods. Selecting parameters to parameterize the scenario space is particularly important to achieve good coverage and high efficiency. However, an extensive collection of (relevant) influence parameters is missing, which allows a thorough consideration when selecting parameters regarding specific scenarios. In addition, the general importance definition for individual influence parameters is not provided, regarding the potential influence of their variations on the safety effectiveness of ADAS, which can also be used as a reference while selecting parameters. Combining knowledge from different sources (the published literature, standardized test scenarios, accident analysis, autonomous vehicle disengagement, accident reports, and specific online surveys), this paper has summarized, in total, 94 influence parameters, given the general definitions of importance for 77 influence parameters based on cluster analysis algorithms. The list of influence parameters provides researchers and system developers a comprehensive basis for pre-selecting influence parameters for evaluating the safety effectiveness of ADAS by virtual scenario-based testing and helps check whether certain influence parameters can be a meaningful extension for the evaluation.

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The occupant kinematics occurring at a lane change maneuver affects the local ride quality. The precise analysis of the occupant kinematics requires a comprehensive understanding of the physiologic response to human body as well as the vehicle kinematics. A series of vehicle-based tests also confirmed that the alertness level of vehicle occupants is one of the important biomechanical elements. Therefore, it is necessary to have a virtual human body model (HBM), an occupant surrogate at CAE design process, with active muscle forces to represent the reflexive response of human beings. An active human body model that produces joint torques with PID closedloop control as mimicking a bracing action to keep the sitting posture against the external jerk has been developed. In this study, this active human body model is validated against the subject test by simulating the similar occupant kinematics at a single lane change maneuvers. To further verify the use of active HBM as a design tool, an artificial lane change maneuver with a reduced lateral jerk is fabricated and good matching occupant kinematics are predicted.