Abstract

Automated vehicles and advanced driver-assistance systems require an accurate prediction of future traffic scene states. The tendency in recent years has been to use deep learning approaches for accurate trajectory prediction but these approaches suffer from computational complexity, dependency on a specific environment/dataset, and lack of insight into vehicle interactions. In this paper, we aim to address these limitations by proposing a Dual Learning Model (DLM) using lane occupancy and risk maps for vehicle trajectory prediction. To understand the spatial interactions of road users, make the model independent of the environment, and consider inter-vehicle distances, we embed an Occupancy Map (OM) into the trajectory prediction model. We also utilise a traffic scene Risk Map (RM) to explicitly consider a comprehensive definition of risk based on Time-to-Collision in the traffic scene. These two features employed in the encoder-decoder architecture improve system accuracy with less complexity and provide insight into the interaction between all road users. The experiment has been conducted on two different naturalistic highway driving datasets (i.e., NGSIM and HighD) demonstrating algorithm independence from a single environment. Comparison results indicate that the DLM achieves a more accurate trajectory prediction with a less complex structure compared with existing approaches in terms of RMS prediction error, which indicates the effectiveness of DLM in such a context.

Highlights

  • Each year, 1.35 million people die, and as many as 50 million are injured and experience long-term disability in road crashes

  • We propose a Dual Learning Model (DLM) for vehicle trajectory prediction that identifies salient information in an Occupancy Map (OM) and a Risk Map (RM) to learn about inter-vehicle interaction and associated risk within the traffic scene in an unsupervised manner

  • We report the trajectory prediction accuracy with the Root of the Mean Square Error (RMSE) metric since it has been widely used in previous work [2], [11], [17], [28]

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Summary

Introduction

1.35 million people die, and as many as 50 million are injured and experience long-term disability in road crashes. To reduce road crash fatalities and have a safer and more efficient transportation system, automated vehicles and driver-assistance systems have become a promising solution. Vehicle trajectory prediction is required in various areas of transportation such as automated vehicles and driving assistance systems. Physics-based approaches have a short time prediction (i.e., less than a second). These approaches assume a constant speed and orientation for the vehicles and possess the lowest degree of abstraction in trajectory prediction. These applications are the most common and simplest techniques using dynamic and kinematic models for trajectory prediction [6]. One of the weaknesses of these systems is that they cannot predict change in vehicle motion caused by manoeuvres or change because of external factors, for instance change to front vehicle speed

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