Risk Prediction for Curve Speed Warning by Considering Human, Vehicle, and Road Factors

Abstract

Current curve speed warning systems take into account mostly vehicle and road factors but not driver behavior. The systems aim at detecting sideslips of small cars on curves without consideration of rollovers for vehicles with an elevated center of gravity. In this study, a curve speed model that considers human, vehicle, and road factors is built to prevent not only sideslips but also rollover accidents for vehicles with an elevated center of gravity. In addition, a risk prediction model is presented to judge accident risk levels and determine levels of warning. Finally, the effectiveness of the presented system is validated with one skilled driver who carries out one test through a simulator under different curve scenarios. To verify the system, data from simulator tests were collected for offline checking of the system. The data were used to calculate safe speeds by using the curve speed model and to determine the levels of risk based on the risk prediction model. The results show that the system is highly compatible with the skilled driver in terms of warning accuracy and timing. Specifically, the correct alarm rate (i.e., the driver brakes and the system’s alarm goes off) of the system is 83.57% and the error alarm rate (i.e., the driver does not brake but the system’s alarm goes off) is 9.79%. Moreover, more than 80% of the time the difference between the system warning time and the operating time of the skilled driver is less than 2 s.