/Monte Carlo Sampling Based Imminent Collision Detection Algorithm

Monte Carlo Sampling Based Imminent Collision Detection Algorithm

Peng Chang and Christoph Mertz
Conference Paper, International Conference on Transportation Information and Safety, August, 2017

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Imminent collision detection is an important problem for automotive safety, and is critical for driving assistance systems and fully autonomous vehicles. Imminent collision detection systems require very low false alarm rate, due to the potential outcome of the detection result. Most current approaches are based on Kalman filter or its variants and tend to have degraded accuracy when the underlying noise models deviate from the Gaussian assumption. In this paper, we present a Monte Carlo sampling based Imminent Collision Detection algorithm (MCICD) to achieve improved accuracy by faithfully modeling the noise distributions of the sensor measurements. We further demonstrate a Monte Carlo sampling framework to perform the FPR/FNR analysis for any given collision detection system, as the criterion to evaluate the performance of different collision detection approaches. Experiments with synthetic data and a laser scanner based prototype system have been conducted to validate our approach.

Received the best paper award

BibTeX Reference
author = {Peng Chang and Christoph Mertz},
title = {Monte Carlo Sampling Based Imminent Collision Detection Algorithm},
booktitle = {International Conference on Transportation Information and Safety},
year = {2017},
month = {August},
keywords = {collision avoidance, Monte Carlo method, noise = distribution, sampling method},