Abstract

In this contribution we propose methods for vehicle detection and tracking for the Advanced Driver Assistance Systems (ADAS) that work under extremely adverse weather conditions. Most of the state-of-the-art vehicle detection and tracking methods are based either on appearance based vehicle recognition or on extraction and tracking of dedicated image key points. Visibility deterioration due to rain drops and water streaks on the windshield, swirling spray, and fog lead to a drastic performance reduction or even to a complete failure of these approaches. In this contribution we propose several methods for coping with these phenomena. In addition to an extension of the feature-based tracking method, which copes with outliers and temporarily disappearing key points, we present a detection and tracking method based on search for vehicle rear lights and whole rear views in the saturation channel. Utilization of symmetry operators and search space restriction allows to detect and track vehicles even in pouring rain conditions. Furthermore, we present two applications of the above-described methods. Estimation of the strength of spray produced by preceding vehicles allows to draw conclusions about the overall visibility conditions and to adjust the intensity of one's own rear lights. Besides, a restoration of deteriorated image regions becomes possible.

Abstract

Weather conditions are widely acknowledged to contribute to the occurrence of congestion on motorway traffic by influencing both traffic supply and traffic demand. To the best of our knowledge, this is the first paper that explicitly integrates supply and demand effects in predicting the influence of adverse weather conditions on the probability of occurrence of congestion. Traffic demand is examined by conducting a stated adaptation experiment, in which changes in travel choices are observed under adverse weather scenarios. Based on these choices, a Panel Mixed Logit model is estimated. Supply effects are taken into account by examining the influence of precipitation on motorway capacity. Based on the Product Limit Method, capacity distribution functions are estimated for dry weather, light rain and heavy rain. With the developed model to integrate the supply and demand effects breakdown probabilities can be calculated for any given traffic demand and capacity. The results show that rainfall leads to a significant increase in the probability of traffic breakdown at bottleneck locations. Interestingly the probability of a breakdown at these bottleneck locations is predicted to be slightly higher in light rain (98.7%) than in heavy rain (95.7%) conditions, which is the result of the higher traffic demand in light rain conditions. Based on the results presented in this paper, it can be recommended to always incorporate both supply and demand effects in the predictions of motorway breakdown probabilities due to adverse weather conditions to improve the validity of the predictions. © 2015 Editorial Board EJTIR. All rights reserved.