Risk assessment at road intersections: Comparing intention and expectation

Abstract

Abstract-Intersections are the most complex and hazardous areas of the road network, and 89% of accidents at intersection are caused by driver error. We focus on these accidents and propose a novel approach to risk assessment: in this work dangerous situations are identified by detecting conflicts between intention and expectation, i.e. between what drivers intend to do and what is expected of them. Our approach is formulated as a Bayesian inference problem where intention and expectation are estimated jointly for the vehicles converging to the same intersection. This work exploits the sharing of information between vehicles using V2V wireless communication links. The proposed solution was validated by field experiments using passenger vehicles. Results show the importance of taking into account interactions between vehicles when modeling intersection situations.

Multi-classification of Driver Intentions in Yielding Scenarios

“…However, this serves mostly as a way to avoid transient false detections and a separate threshold variable is fit in order to perform classification, while the quality of the estimated probability is not evaluated. Bayesian network models, which give probability estimates of intentions, have been explored by [6], [7] and others. These types of probabilistic models have the potential to explicitly encode domain knowledge but often one needs to include classification type models as local distributions in the network which are similar to the algorithms evaluated in this paper.…”

Warning of Potential Collision for Vehicles

“…Lefèvre et al [11] pointed out that risk assessment at intersections is possible by comparing intention and expectation. Liebner et al [12] also used drivers' intent inference at intersections.…”

Modelling stop intersection approaches using Gaussian processes

“…For example, in [9] the system needs to estimate the driver intention to stop. With a relaxed driver, the intention not to stop can be detected earlier with individual profiles than with generic profiles.…”

“…The thresholds are arbitrary set and are not driver dependant. The authors in [9] use speed profiles within a Dynamic Bayesian Network, implemented in cooperative cars for risk assessment at road intersections. The cruise speed of the host vehicle is used for the estimation of the expected deceleration profile, however it is assumed that all driver react in the same manner.…”