Study on Crash Injury Severity Prediction of Autonomous Vehicles for Different Emergency Decisions Based on Support Vector Machine Model

Abstract

Motor vehicle crashes remain a leading cause of life and property loss to society. Autonomous vehicles can mitigate the losses by making appropriate emergency decision, and the crash injury severity prediction model is the basis for autonomous vehicles to make decisions in emergency situations. In this paper, based on the support vector machine (SVM) model and NASS/GES crash data, three SVM crash injury severity prediction models (B-SVM, T-SVM, and BT-SVM) corresponding to braking, turning, and braking + turning respectively are established. The vehicle relative speed (REL_SPEED) and the gross vehicle weight rating (GVWR) are introduced into the impact indicators of the prediction models. Secondly, the ordered logit (OL) and back propagation neural network (BPNN) models are established to validate the accuracy of the SVM models. The results show that the SVM models have the best performance than the other two. Next, the impact of REL_SPEED and GVWR on injury severity is analyzed quantitatively by the sensitivity analysis, the results demonstrate that the increase of REL_SPEED and GVWR will make vehicle crash more serious. Finally, the same crash samples under normal road and environmental conditions are input into B-SVM, T-SVM, and BT-SVM respectively, the output results are compared and analyzed. The results show that with other conditions being the same, as the REL_SPEED increased from the low (0–20 mph) to middle (20–45 mph) and then to the high range (45–75 mph), the best emergency decision with the minimum crash injury severity will gradually transition from braking to turning and then to braking + turning.

A Study of Common Principles for Decision-Making in Moral Dilemmas for Autonomous Vehicles

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Reinforcement Learning Based Passengers Assistance System for Crowded Public Transportation in Fog Enabled Smart City

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Study on Severity and Influencing Factors of Injury at Intersections

“…In this study, the vehicle collision accident data comes from the 2015 GES data set in the NASS database of the NHTSA department, which is mainly composed of three sub-data sets, namely, the accident data set, the vehicle data set and the collision participant data set [6][7]. The accident data set includes road conditions, environmental conditions and accident-related characteristics, and the vehicle data set includes a large number of characteristic variables of the vehicles involved in the accident, such as pre-collision speed, vehicle obstacle avoidance decision, etc., the collision participant data set includes a large number of characteristic variables of accident participants (drivers, passengers, pedestrians, cyclists, etc.…”