Trajectory Prediction-Based Local Spatio-Temporal Navigation Map for Autonomous Driving in Dynamic Highway Environments

Autonomous driving, including intelligent decision-making and path planning, in dynamic environments (like highway) is significantly more difficult than the navigation in static scenarios because of the additional time dimension. Therefore, correlating the time dimension and the space dimension through prediction to create a spatio-temporal navigation map can make decision-making and path planning in such kinds of environment much easier. In this article, NGSIM data is analysed and processed from the perspective of the ego-vehicle (using the data as an ego-vehicle's perception results). Based on the data, we develop an LSTM (Long-Short Term Memory)based framework to predict possible trajectories of multiple surrounding vehicles within a certain range of the ego-vehicle. Then, the multiple predicted trajectories in a series of continuous dynamic highway scenes are projected into a spatio-temporal domain to create an octree map. Thus, dynamic targets and static obstacles can be unified into the same domain or map so that the dynamic disturbance problem for autonomous driving in highway environments can be resolved. Experimental results show that the proposed model is capable of predicting all the future trajectories around the ego-vehicle efficiently and the corresponding spatiotemporal map can be generated accurately in different dynamic scenarios.

Automated summary

This article analyzes NGSIM data and develops an LSTM-based framework to efficiently predict future trajectories of surrounding vehicles, projecting them into a spatio-temporal domain to create an octree map, resolving the issue of dynamic disturbances in autonomous driving.