Utilizing B-Spline Curves and Neural Networks for Vehicle Trajectory Prediction in an Inverse Reinforcement Learning Framework

Mohammad Sadegh Jazayeri,Arash Jahangiri

doi:10.3390/jsan11010014

Mohammad Sadegh Jazayeri, Arash Jahangiri

Open Access

https://doi.org/10.3390/jsan11010014

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Abstract

The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems that aim to identify critical events such as near-crash situations and traffic violations. In a connected environment, important information about these critical events can be communicated to road users or the infrastructure to avoid or mitigate potential crashes. Intersections require special attention in this context because they are hotspots for crashes and involve numerous and complex interactions between road users. In this work, we developed an advanced machine learning method for trajectory prediction using B-spline curve representations of vehicle trajectories and inverse reinforcement learning (IRL). B-spline curves were used to represent vehicle trajectories; a neural network model was trained to predict the coefficients of these curves. A conditional variational autoencoder (CVAE) was used to generate candidate trajectories from these predicted coefficients. These candidate trajectories were then ranked according to a reward function that was obtained by training an IRL model on the (spline smoothed) vehicle trajectories and the surroundings of the vehicles. In our experiments we found that the neural network model outperformed a Kalman filter baseline and the addition of the IRL ranking module further improved the performance of the overall model.

Highlights

The problem of trajectory prediction involves forecasting the path a vehicle is going to take given its past trajectory and surroundings
The neural network had the following input features: the x and y distance from the center of the approach from which the vehicle entered the intersection to the centers of the three road segments by which the vehicle can exit the intersection, the distance of the vehicle from the center of the approach, velocity before entering the intersection, vehicle acceleration before entering the intersection, vehicle heading before entering the intersection, average vehicle velocity over the monitoring period (2 s in the final model), average vehicle acceleration over the monitoring period, and the turning movements allowed for the lane that the vehicle was in
An inverse reinforcement learning (IRL) model was trained in the following manner: the B-spline smoothed trajectories of the vehicles were embedded into images containing the geometry of the intersection, as well as the trajectories of the other vehicles present at the intersection For the reward function approximator, we used a pretrained convolutional neural network, namely MobileNetV2, with the final softmax layer removed

Summary

Introduction

The problem of trajectory prediction involves forecasting the path a vehicle is going to take given its past trajectory and surroundings. A solution to this problem would have applications in surrogate safety analysis [1], evaluating road safety, and infrastructurebased safety systems for providing early crash warnings [2] Solving this problem is of critical importance for advanced driver assistance systems (ADAS) [3,4,5] and autonomous vehicles (AV) [3,6,7]. When cast as a control problem, i.e., a problem of finding the correct control behavior, solving the problem of trajectory prediction would be equivalent to training a model to drive similar to human drivers. This enables applications where human-like driving is desired. We focused solely on the prediction problem

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