Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning

TL;DR

This paper introduces a machine learning method using Random Forests to efficiently predict future traffic scenarios with probabilistic occupancy grids, enhancing real-time vehicle safety systems by modeling uncertainties in traffic behavior.

Contribution

It presents a novel grid-based representation and a machine learning approach to compute Predicted-Occupancy Grids, reducing computational load compared to traditional model-based methods.

Findings

Machine learning approach achieves promising accuracy.

Simulation results suggest real-time applicability.

Improves modeling of traffic uncertainties for safety systems.

Abstract

This paper presents a method to predict the evolution of a complex traffic scenario with multiple objects. The current state of the scenario is assumed to be known from sensors and the prediction is taking into account various hypotheses about the behavior of traffic participants. This way, the uncertainties regarding the behavior of traffic participants can be modelled in detail. In the first part of this paper a model-based approach is presented to compute Predicted-Occupancy Grids (POG), which are introduced as a grid-based probabilistic representation of the future scenario hypotheses. However, due to the large number of possible trajectories for each traffic participant, the model-based approach comes with a very high computational load. Thus, a machine-learning approach is adopted for the computation of POGs. This work uses a novel grid-based representation of the current state of the traffic scenario and performs the mapping to POGs. This representation consists of augmented cells in an occupancy grid. The adopted machine-learning approach is based on the Random Forest algorithm. Simulations of traffic scenarios are performed to compare the machine-learning with the model-based approach. The results are promising and could enable the real-time computation of POGs for vehicle safety applications. With this detailed modelling of uncertainties, crucial components in vehicle safety systems like criticality estimation and trajectory planning can be improved.