Quantification and Pictorial Expression of Driving Status Domain Boundaries for Autonomous Vehicles in LTAP/OD Scenarios

Abstract

The ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite direction (LTAP/OD) scenarios are subdivided into comfort, discomfort, extreme, and crash, and the boundaries of each status domain are quantified and visualized. First, real unprotected left turn road segments are chosen for the actual vehicle testing. Subjective passenger comfort evaluation results and objective motion state data of vehicles during the experiment are organized and analyzed by statistics. In addition, the pictorials are plotted to determine the comfort and extreme status domain boundaries based on motion state parameters. Second, based on the unprotected left turn kinematic analysis and modeling, as well as a skilled driver risk perception and operational model, the Safe Collision Plots (SCP) of conflicting vehicles in LTAP/OD scenarios are quantified and expressed as pictorial examples. By combining objective motion parameters and passenger experience, intuitively quantifying each driving status domain of vehicles can provide more fine-grained information for the design parameters of ADAS and autonomous vehicles and increase public trust and acceptance of them.

Keywords

• ADAS
• autonomous vehicles
• driving status domain
• motion state parameters
• passenger experience
• boundary quantification