Driving risk classification based on experts evaluation

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

et al. 2011

Self Cite

In this paper a novelty method to combine knowledge of traffic safety experts, in order to detect driving risk situations, is presented. A set of driving sessions were executed in a very realistic truck simulator where several magnitudes and visual information from the vehicle, driver and road were collected. Two kind of experiments were designed: controlled driving sessions (where several risky situations were induced), and natural driving sessions (where a natural driving behavior was expected). A group of traffic safety experts were consulted to evaluate the driving risk in each session. The information acquired from the traffic safety experts was used to develop a methodology to combine the information and to define a set of driving risk models. The developed system detected most of the induced risk situations besides of several non-induced risk situations. The methodology presented in this paper can be used to obtain a driving risk ground truth in order to compare and evaluate risk detection algorithms and to analyze the influence of vehicle, driver and road variables on the driving risk.

Section: B Knowledge Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining traffic safety knowledge for driving risk detection

Siordia

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

et al. 2011

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“…One of the main objectives of CABINTEC is to identify driver's unsuitable behavior and lacks of attention. The data base considered in this paper was obtained in a very realistic and immersive cabin truck simulator (see [8] for a complete description). The cabin is a real cabin truck placed over pneumatically devices, so real movements are reproduced.…”

Section: Technical Setupmentioning

confidence: 99%

Detection and Tracking of Driver’s Hands in Real Time

Crespo

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

et al. 2010

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Abstract. In this paper a complete driver's hands detection and tracking system suitable for working in real time conditions has been developed. The proposed system has been successfully tested in close-real world conditions in different scenarios on a very realistic and immersive cabin truck simulator. A database of 24 video sequences monitoring the driving task in different circuits, illumination conditions and video resolutions has been obtained. The hands detection rate and the computational times needed to process each frame are presented. The proposed system has proven to be high accurate and fast enough to work in real time conditions. In the future, the selected algorithm will be included as part of an automotive compliance embedded system placed in a real truck cabin.

“…Nevertheless, given the high number of factors involved in driver distraction, there is no general definition of driving risks. Further, the characterization of risky or safe situations for the driving task is hard to achieve due to its subjective entity [3]. In the literature, there are examples of the characterization of risky situations through the analysis of data collected on driving sessions.…”

Section: Introductionmentioning

confidence: 99%

Optimal experts' knowledge selection for intelligent driving risk detection systems

Siordia

2012 IEEE Intelligent Vehicles Symposium

et al. 2012

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This paper presents a method for the selection of the optimal combination of experts' knowledge needed for the generation of a reliable driving risk ground truth. The driving risk of a controlled driving session, recorded in a highly realistic truck simulator, was evaluated by a large number of traffic safety experts. The risk evaluations were grouped in several clusters in order to find experts with high agreement. Next, a method for the selection of the optimal experts' evaluations is proposed. We found, through the experiments performed in this study, that a low number of experts are sufficient for the properly detection of driving risks. In addition, we show some of the advantages of the consideration of traffic safety experts' knowledge for the generation of a driving risk ground truth.