Rollover Warning for Articulated Heavy Vehicles Based on a Time-to-Rollover Metric

Abstract

A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated heavy vehicle. The TTR metric accurately “counts-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately assessed. There are two conflicting requirements in the implementation of TTR. On the one hand, a model significantly faster than real-time is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the design process is illustrated in an example. First, a simple yet reasonably accurate yaw∕roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simple model. The NN takes the TTR generated by the simple model, vehicle roll angle, and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achieved across all the driving scenarios we tested.