Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

Laugier, Christian; Paromtchik, I.E.; Perrollaz, Mathias; Yong, M. Y.; Yoder, John-David; Tay, Christopher; Mekhnacha, Kamel; Nègre, Amaury · 2011 · OpenAlex-citations

DOI: 10.1109/mits.2011.942779

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Summary

This paper addresses the challenge of assessing collision risks in dynamic urban driving scenarios, where traditional metrics like Time-to-Collision (TTC) often fail due to their inability to account for complex road contexts and uncertain future behaviors of other agents. The authors argue that TTC produces false negatives in intersections with stopped vehicles and false positives on curved roads, necessitating a probabilistic approach that predicts the likely future actions of traffic participants. The goal is to improve driving safety by providing a robust interpretation of the traffic scene through sensor fusion and risk estimation. The proposed method utilizes a probabilistic framework implemented on an experimental platform comprising a Lexus vehicle and a driving simulator. The system integrates data from on-board stereo-vision cameras and lidar sensors. Environment modeling is performed using a Bayesian Occupancy Filter (BOF), which creates a spatio-temporal grid representation of the scene. This filter estimates both the occupancy probability and velocity distribution for each cell, handling uncertainties and enabling parallel computation. To detect and track dynamic objects, the system employs a Fast Clustering and Tracking Algorithm (FCTA), which associates clusters in the occupancy grid with tracked objects using region-of-interest predictions. Collision risks are modeled as stochastic variables, with future behaviors of other agents predicted using Hidden Markov Models (HMM) and Gaussian Processes (GP). The experimental results demonstrate the feasibility and relevance of this approach for real-time collision risk assessment. The BOF effectively fuses sensor data to distinguish between static and dynamic entities, while the FCTA successfully tracks objects despite occlusions and ambiguities. The probabilistic prediction of agent behaviors allows for a more accurate assessment of potential threats compared to deterministic methods. The system successfully processed visual, telemetric, and inertial data to estimate collision risks for the ego-vehicle in various traffic scenarios. The implementation leveraged parallel computation capabilities, ensuring the methods were suitable for real-time applications. The significance of this work lies in its comprehensive probabilistic framework for interpreting dynamic traffic scenes, which overcomes the limitations of current risk assessment metrics. By modeling uncertainties in sensor data and predicting future behaviors, the system provides a more reliable basis for driver assistance and automated maneuvering. The integration of BOF and FCTA offers a scalable solution for environment monitoring, contributing to the development of safer intelligent transportation systems. The study validates that probabilistic analysis can effectively mitigate collision risks in complex urban environments, supporting the advancement of zero-collision driving technologies.

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• telematics crash prediction