Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic

TL;DR

This paper presents a new machine learning architecture combining Stacked Denoising Autoencoders and Random Forests to predict future traffic scenarios from current occupancy data, aiding autonomous driving safety.

Contribution

The paper introduces a novel hybrid architecture for predicting probabilistic traffic occupancy grids, improving accuracy and computational efficiency over existing methods.

Findings

The proposed architecture outperforms existing models in accuracy.

It reduces computational time compared to previous approaches.

Validated through simulations with promising results.

Abstract

This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future space-time representation of the traffic scenario, first the type of traffic scenario is identified and then the machine learning algorithm maps the current state of the scenario to possible future states. The input to the machine learning algorithms is the current state representation of a traffic scenario, termed as the Augmented Occupancy Grid (AOG). The output is the probabilistic space-time representation which includes uncertainties regarding the behaviour of the traffic participants and is termed as the Predicted Occupancy Grid (POG). The novel architecture consists of two Stacked Denoising Autoencoders (SDAs) and a set of Random Forests. It is then compared with the other two existing architectures that comprise of SDAs and DeconvNet. The architectures are validated with the help of simulations and the comparisons are made both in terms of accuracy and computational time. Also, a brief overview on the applications of POGs in the field of active safety is presented.