Physically constrained short-term vehicle trajectory forecasting with naive semantic maps

TL;DR

This paper introduces a CNN-LSTM model that leverages semantic maps to improve short-term vehicle trajectory prediction in urban environments, accounting for physical constraints and enabling longer horizon forecasts.

Contribution

The novel approach combines semantic map features with motion data in a CNN-LSTM architecture to enhance trajectory prediction accuracy considering physical constraints.

Findings

Model accurately predicts trajectories considering road boundaries.

Effective for longer prediction horizons than trained for.

Validated on urban datasets with challenging scenarios.

Abstract

Urban environments manifest a high level of complexity, and therefore it is of vital importance for safety systems embedded within autonomous vehicles (AVs) to be able to accurately predict the short-term future motion of nearby agents. This problem can be further understood as generating a sequence of future coordinates for a given agent based on its past motion data e.g. position, velocity, acceleration etc, and whilst current approaches demonstrate plausible results they have a propensity to neglect a scene's physical constrains. In this paper we propose the model based on a combination of the CNN and LSTM encoder-decoder architecture that learns to extract a relevant road features from semantic maps as well as general motion of agents and uses this learned representation to predict their short-term future trajectories. We train and validate the model on the publicly available dataset that provides data from urban areas, allowing us to examine it in challenging and uncertain scenarios. We show that our model is not only capable of anticipating future motion whilst taking into consideration road boundaries, but can also effectively and precisely predict trajectories for a longer time horizon than initially trained for.