Abstract
In this study, to explore the demand characteristics of autonomous coaches in mixed traffic flow, two sections of an expressway were selected for vehicle experiments. With personification as the goal, sensors of the ego-vehicle were used to collect naturalistic car-following behavior data of surrounding coaches. After analyzing the naturalistic car-following data, car-driving behavior characteristics of the coach drivers were acquired. The analysis results indicate that the overall car-following processes of coaches tend to be relatively stable, and most of the car-following processes are in the conditions of higher velocity, smaller acceleration, and smaller relative velocity than the front vehicle. The expected following spacing and time headway of the coach drivers increase with the increase of velocity. By analysis and comparison with existing safe following models, it is found that while the safety and the warning rate based on the car-following coach safety distance model are higher, the warning rate of the car-following time headway model at the relatively higher velocity is lower. The coach safety distance model established in this study considers the car-following risk and the acceptance rate of the drivers to the warning system. This model conforms to the driver’s car-following characteristics and fulfills the requirements of collision avoidance. Applying the safety distance model to the autonomous coach would effectively improve the anthropopathic behavior characteristics of the autonomous coach. Additionally, the abnormality caused by the interference of other vehicles in mixed traffic flow of autonomous and human vehicles is successfully avoided under the premise of ensuring the safe driving of the autonomous coach.
Highlights
The car-following data of coaches on an expressway was collected by sensors on the ego-vehicle, and their car-following behavior characteristics were explored
In this relative velocity interval, the coach safety distance model is specified as the critical warning model for safe braking, and has a more conservative warning spacing
As determined by the characteristics of coach drivers’ natural car-following behaviors, and based on car-following behavior data collected by sensors on the ego-vehicle, the natural car-following data of coaches were modeled from the aspect of rear-end warning, and the car-following behavior characteristics of coach drivers were acquired
Summary
The car-following data of coaches on an expressway was collected by sensors on the ego-vehicle, and their car-following behavior characteristics were explored. Kusano et al quantified the TTC values of drivers of different ages when braking during car-following, by analyzing 72123 real driving scenes from 64 drivers Used this data to optimize the forward collision warning system to increase its acceptability [7]. D. STRUCTURE OF THE METHOD Firstly, the car-following data of coaches on an expressway was collected by sensors on the ego-vehicle. The cumulative THW frequencies at P25, P50, and P75 are 1.50 s, 2.23 s, and 3.09 s, respectively, and the mean time headway is 2.42 s
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