Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
This addresses motion prediction for autonomous vehicles, but it is incremental as it builds on existing LSTM-based methods with maneuver integration.
The paper tackles the problem of predicting future motion of surrounding vehicles for autonomous driving by proposing an LSTM model that assigns confidence to maneuvers and outputs multi-modal distributions. It shows improvement in RMS prediction error on NGSIM datasets compared to prior art.
To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles. Multiple interacting agents, the multi-modal nature of driver behavior, and the inherent uncertainty involved in the task make motion prediction of surrounding vehicles a challenging problem. In this paper, we present an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways. Our model assigns confidence values to maneuvers being performed by vehicles and outputs a multi-modal distribution over future motion based on them. We compare our approach with the prior art for vehicle motion prediction on the publicly available NGSIM US-101 and I-80 datasets. Our results show an improvement in terms of RMS values of prediction error. We also present an ablative analysis of the components of our proposed model and analyze the predictions made by the model in complex traffic scenarios.