Loss of Control Prediction for Motorcycles during Emergency Braking Maneuvers Using a Supervised Learning Algorithm

Abstract

The most common evasive maneuver among motorcycle riders and one of the most complicated to perform in emergency situations is braking. Because of the inherent instability of motorcycles, motorcycle crashes are frequently caused by loss of control performing braking as an evasive maneuver. Understanding the motion conditions that lead riders to start losing control is essential for defining countermeasures capable of minimizing the risk of this type of crashes. This paper provides predictive models to classify unsafe loss of control braking maneuvers on a straight line before becoming irreversibly unstable. We performed braking maneuver experiments in the field with motorcycle riders facing a simulated emergency scenario. The latter involved a mock-up intersection in which we generated conflict events between the motorcycle ridden by the participants and an oncoming car driven by trained research staff. The data collected comprises 165 braking trials (including 11 trials identified as loss of control) with 13 riders representing four categories of braking skill, ranging from beginner to expert. Three predictive models of loss of control events during braking trials, going from a basic model to a more advanced one, were defined using logistic regressions as supervised learning methods and using the area under the receiver operating characteristic (ROC) curve as a performance indicator. The predictor variables of the models were identified among the parameters of the vehicle kinematics. The best model predicted 100% of the loss of control and 100% of the full control cases. The basic and the more advanced supervised models were adapted for loss of control identification with time series data, and the results detecting in real-time the loss of control events showed excellent performance as well as with the supervised models. The study showed that expert riders may maintain stability under dynamic conditions that normally lead less skilled riders to a loss of control or falling events. The best decision thresholds of the most relevant kinematic parameters to predict loss of control have been defined. The thresholds of parameters that typically characterize the loss of control such as the yaw rate and front-wheel lock duration were dependent on the rider skill levels. The peak-to-root-mean-square ratio of roll acceleration was the most robust parameter for identifying loss of control among all skill levels.