Intelligent transportation systems: When is safety information relevant?

Transportation Research Record

et al. 2015

Self Cite

Safety warning applications from a vehicular ad hoc network aim to disseminate alerts about dangerous events on the road with wireless communication technology and, when necessary, warn drivers receiving such alerts. Examples include the emergency electronic brake light or the highway merge warning. A major issue with such applications is false warnings, which lessen any safety benefits the applications provide. A high number of false warnings will lead to driver desensitization and reduce any potential safety benefits. Therefore any received alert has to be evaluated in terms of its relevance for the given vehicle. However, the relevance depends on a combination of many factors and is specific to a given application, so defining an estimator is difficult. A machine-learning method based on the principle of observe-driver-and-learn is proposed for finding relevance estimators. This method is evaluated for its effectiveness with three safety applications: electronic emergency brake lights, the highway merge warning, and the control loss warning.

Section: Relevant Literaturementioning

confidence: 99%

Observe-Driver-and-Learn Platform for Relevance Estimation in Safety Warning Applications from Vehicular Ad Hoc Network

Lin

Transportation Research Record

et al. 2015

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“…The results showed that, by learning of parking availability, vehicles made better selections among the different parking availability reports and lowered the time that it took them to find a parking space. In [15], the machine learning approach was proposed for use in the EEBL application. This paper presents the further development of that work by showing how the machine learning methods can handle multiple lane roads, presenting further testing of wireless communication parameters, and providing a methodology for setting the system parameters.…”

Section: Relevant Workmentioning

confidence: 99%

Estimating Relevance for the Emergency Electronic Brake Light Application

IEEE Trans. Intell. Transport. Syst.

et al. 2012

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In this paper, we compare two methods of estimating relevance for the emergency electronic brake light application: One method uses an analytically derived formula based on the minimum safety gap that is required to avoid a collision, whereas the other method uses a machine learning approach. The application works by disseminating reports about vehicles that perform emergency deceleration in an effort to warn drivers about the need to perform emergency braking. Vehicles that receive such reports have to decide on whether the information contained in the report is relevant to the driver and warn the driver if that is the case. Common ways of determining relevance are based on the lane or direction information, but using only these attributes can lead to many false warnings, which can desensitize the driver. Desensitized drivers may ignore warnings or completely turn off the system, thus eliminating any safety benefits of the application. We show that the machine learning method, compared with the analytically derived formula, can significantly reduce the number of false warnings by learning from the actions that drivers take after receiving a report. The methods were compared using simulated experiments with a range of traffic and communication parameters.

“…The tools provide an ability to evaluate the safety benefit of the application for the learned relevance estimation model. The platform is based on the Observe-Driverand-Learn (ODaLe) principle, which is a method for learning the relevance of information that has previously been used for safety [4] and non-safety applications [5]. The idea is to observe the driver's reaction after a report was generated by the application and use this information as an input to a machine learning algorithm.…”

Section: The Platformmentioning

confidence: 99%

A methodology for the development of novel VANET safety applications

Proceedings of the Ninth ACM International Workshop on Vehicular Inter-Networking, Systems, and Applications

et al. 2012

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1We present a methodology for the development of passive ITS safety applications that aim to disseminate reports about dangerous events on the road. Examples of such applications include the emergency electronic brake light or the highway merge warning. A major issue with such applications is the decision of when a warning should be shown. Since the recipient vehicle may be far away from where the dangerous event occurred, a large number of false warnings may be shown to the drivers. This leads to driver desensitization which may reduce the safety benefits. While previous research has provided a way of handling false warnings by estimating the relevance of the reports, these methods do not take into all the important factors and are not easily adaptable for novel applications. In this paper, we propose a simulation platform for developing and evaluating relevance estimators for passive ITS safety applications that can be utilized for developing novel applications. The paper provides examples of the effectiveness of this platform on three previously proposed applications.