Abstract
A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated vehicle. Ideally, a TTR metric will accurately “count-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately indicated. To implement TTR in real-time, there are two conflicting requirements. On the one hand, a faster-than-real-time model 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 whole process is illustrated in a design 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 simplified real-time model. The NN takes the TTR generated by the simplified 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 achievable across all the driving scenarios we tested.
Volume Accuracy Problem