A Fast and Map-Free Model for Trajectory Prediction in Traffics

TL;DR

This paper introduces a fast, map-free trajectory prediction model for traffic scenarios that effectively captures spatial-temporal interactions among agents, outperforming existing methods in accuracy and efficiency.

Contribution

The proposed model is the first to combine attention, LSTM, graph convolution, and transformers for map-free trajectory prediction, achieving superior performance and speed.

Findings

Outperforms existing map-free methods in accuracy

Exceeds most map-based methods on Argoverse dataset

Offers faster inference speed than baseline models

Abstract

To handle the two shortcomings of existing methods, (i)nearly all models rely on high-definition (HD) maps, yet the map information is not always available in real traffic scenes and HD map-building is expensive and time-consuming and (ii) existing models usually focus on improving prediction accuracy at the expense of reducing computing efficiency, yet the efficiency is crucial for various real applications, this paper proposes an efficient trajectory prediction model that is not dependent on traffic maps. The core idea of our model is encoding single-agent's spatial-temporal information in the first stage and exploring multi-agents' spatial-temporal interactions in the second stage. By comprehensively utilizing attention mechanism, LSTM, graph convolution network and temporal transformer in the two stages, our model is able to learn rich dynamic and interaction information of all agents. Our model achieves the highest performance when comparing with existing map-free methods and also exceeds most map-based state-of-the-art methods on the Argoverse dataset. In addition, our model also exhibits a faster inference speed than the baseline methods.