Monte Carlo based Threat Assessment: Analysis and Improvements

This paper presents improvements and extensions of a previously presented threat assessment algorithm. The algorithm uses Monte Carlo simulation to find threats in a road scene. It is shown that by using a wider sample distribution and only apply the most likely samples from the Monte Carlo simulation for the threat assessment, improved results are obtained. By using this method more realistic paths will be chosen by the simulated vehicles and more complex traffic situations will be adequately handled. An improvement of the dynamic model is also suggested, which improves the realism of the Monte Carlo simulations. Using the new dynamic model less false positive and more valid threats are detected.

I. Introduction

Building safer vehicles is a prime concern of todays Automotive Manufacturers. There are currently many automotive collision avoidance systems approaching the market, such as collision mitigation system [1], [2] and collision warning systems [2], [3]. These applications have in common that they try to assess one kind of threat and take action when that specific threat is detected. Broadhurst et al. [4] presents a framework for reasoning about the future motions of multiple objects in a road scene. This method can be used to find threats by predicting the paths of the objects using Monte Carlo simulation. Using the framework presented, in theory any kind of threat could be detected, not as in earlier work only a specific one. Eidehall et al. [5] developed a threat assessment algorithm based on this framework.