Browse Publications Technical Papers 2020-01-0122
2020-04-14

The Design of Safe-Reliable-Optimal Performance for Automated Driving Systems on Multiple Lanes with Merging Features 2020-01-0122

Safety function for automated driving systems including advanced driver assistance systems and autonomous vehicle systems is very important. Inside safety function, predictive judge sub-function should be designed with the consideration of more and more penetration of automated driving vehicles. This paper presents the design on multiple lanes with merging features based on the author's previous Patent JP2019-147944 using predictive time-head-way and time-to-collision maps. In the author's previous work (Model Predictive Control for Hybrid Electric Vehicle Platooning Using Slope Information-Published on IEEE Transactions on Intelligent Transportation Systems), a model predictive control framework was designed. Due to the difficulty to detail the sub-safety function deeply with merging features, few works are found to deal with sensor platforms focusing on rear side, and situations of merging lane side with the consideration of relative relation variations with other vehicles and road border markers. However, performance enhancement is needed assuring 100% safety-reliability-optimality and single-objectivity. Also, platforms of on-board sensors including side and rear view are needed to deal with false negative operations and false positive operations. The optimal operation line model of human factors is designed based on time-head-way (reliability), time-to-collision (safety), and combinations of time-head-way and time-to-collision (optimality). The general theory of model predictive control is used to find the target. The model based methodology is applied to solve the human factor model of risk feeling based on only time-head-way and time-to-collision for the human reaction and acceptance metric. Experimental results validated the effectiveness of the proposed approach. The model parameters can be calibrated internationally by tuning the metric of cooperativeness. The target of the predictive judge sub-function is to move the operation point to the specified area. The predictive judge sub-function on high level is decisive for regulation control to move the operation point from difficult areas to the target area in future.

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