Revised Trajectory Forecasting for Powered Two Wheelers by Roll Angle Prediction with an LSTM Network

Active safety systems for powered two wheelers (PTWs) are considered a key pillar to further reduce the number of accidents and thus of injured riders and fatalities. Enhanced awareness for the current riding situation is required to improve the performance of current systems as well as to enable new ones; this includes the detection of the rider’s intention – the action that is planned by the rider for the short-term future. The prediction of a continuous trajectory for the upcoming seconds of the ride is one way to express rider intention. Our work pursues the prediction of the PTW lateral dynamic state by means of a roll angle trajectory over the upcoming 4 s of riding. It thus considers the special vehicle dynamics characteristics of single-track vehicles that negotiate bends at a roll angle compared to cars. A deep learning (DL) prediction model that is based on a Long-Short Term Memory (LSTM) layer is optimized and trained for this task using a broad on-road riding dataset that focuses on the rural road environment. Inputs to the prediction model are PTW internal signals only, that are measurements of vehicle dynamics, rider inputs, and rider behavior. The latter two groups of signals are non-common for current series production PTWs and were especially added to our test bike before gathering the riding data set. The prediction performance of the optimized DL model is compared to a simple heuristic algorithm using multiple metrics in the roll angle and position trajectory domain. Evaluation on a representative test data set shows a significantly improved detection of rider intention by the DL model in all metrics. Reasonable lateral trajectory accuracy is achieved for at least 2 s of the total 4 s prediction horizon in 99 % of all test cases, given the chosen model architecture and input features. Furthermore, the feature importance of the especially added non-common measurement signals of steering and rider behavior is investigated in an ablation study. It reveals the importance of steering signals in the first second of the prediction horizon whereas the rider behavior signals aid trajectory prediction performance for up to 2.5 s.