Predictions of short-term driving intention using recurrent neural network on sequential data

TL;DR

This paper presents a recurrent neural network-based method for predicting short-term driving intentions from sequential data, aiding autonomous vehicles in decision-making by understanding driver behaviors and intentions.

Contribution

It introduces a novel methodology for building and training predictive systems to accurately forecast driving intentions using sequential data and RNNs.

Findings

Effective prediction of short-term driving intentions demonstrated

Improved decision-making capabilities for autonomous vehicles

Framework captures on-road behavioral characteristics

Abstract

Predictions of driver's intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the fundamental units that constitute more sophisticated driving goals, behaviors, such as overtaking the slow vehicle in front, exit or merge onto a high way, etc. While it is not uncommon that most of the time human driver can rationalize, in advance, various on-road behaviors, intentions, as well as the associated risks, aggressiveness, reciprocity characteristics, etc., such reasoning skills can be challenging and difficult for an autonomous driving system to learn. In this article, we demonstrate a disciplined methodology that can be used to build and train a predictive drive system, therefore to learn the on-road characteristics aforementioned.