Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms

Abstract

Abstract-Road situation analysis in Interactive IntelligentDriver-Assistance and Safety Warning (I 2 DASW) systems involves estimation and prediction of the position and size of various on-road obstacles. Real-time processing, given incomplete and uncertain information, is a challenge for current object detection and tracking technologies. This paper proposed a development framework and novel algorithms for road situation analysis based on driving action behavior, where the safety situation is analyzed by simulating real driving action behaviors. First, we review recent development and trends in road situation analysis to provide perspective for the related research. Second, we introduce a road situation analysis framework, where onboard sensors provide information about drivers, traffic environment, and vehicles. Finally, on the basis of the previous frameworks, we proposed multiple-obstacle detection and tracking algorithms using multiple sensors including radar, lidar, and a camera, where a decentralized track-to-track fusion approach is introduced to fuse these sensors. In order to reduce the effect of obstacle shape and appearance, we cluster lidar data and then classify obstacles into two categories: static and moving objects. Future collisions are assessed by computation of local tracks of moving obstacles using extended Kalman filter, maximum likelihood estimation to fuse distributed local tracks into global tracks, and finally, computation of future collision distribution from the global tracks. Our experimental results show that our approach is efficient for road situation evaluation and prediction.

Towards an Ontology-Based Approach to Safety Management in Cooperative Intelligent Transportation Systems

“…The factors of concern include the characteristics of the road, traffic flow, time/season and the people involved in an accident. The work presented in [10] also considers the factors relating to driver behavior and vehicle dynamics including sensor uncertainty and vehicle state. In this approach, the risk level is assessed at run time by combining traffic rules, vehicle dynamics, and environment prediction.…”

Incremental Online Object Learning in a Vehicular Radar-Vision Fusion Framework

“…4 over x i (t), conditional on w bi staying unchanged (i.e., converged), we have C w bi = λ w bi (5) where C is the covariance matrix of inputs x i (t) over time t and the scalar λ = ∑ t ∥w bi (t)∥∥x i (t)∥. Eq.…”

“…Examples include both fully autonomous driving vehicles [2] [3] [4] and Advanced Safety Driver Assistance Systems (ASDAS) [5] [6], such as adaptive cruise control (ACC), lane departure warning (LDW) and collision avoidance system, etc. The success of intelligent vehicle systems depends on a rich understanding of the complex road environment, which contains many signals and cues that visually convey information, such as traffic lights, road signs, other vehicles, and pedestrians, to name a few.…”

New Robust Obstacle Detection System Using Color Stereo Vision

“…These systems also help drivers to get a large visibility area when the visibility conditions is reduced such as night, fog, snow, rain, ... Obstacle detection systems process data acquired from one or several sensors: radar Kruse et al (2004), lidar Gao & Coifman (2006), monocular vision Lombardi & Zavidovique (2004), stereo vision Bensrhair et al (2002) Cabani et al (2006b) Kogler et al (2006) Woodfill et al (2007), vision fused with active sensors Gern et al (2000) Steux et al (2002) Möbus & Kolbe (2004) Zhu et al (2006) Alessandretti et al (2007) Cheng et al (2007). It is clear now that most obstacle detection systems cannot work without vision.…”