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A Platform for the Development and Evaluation of Passive Safety Applications*
"... Abstract — In this paper, we present a platform for aiding in the development and evaluation of novel ITS passive safety applications. Such applications work by having vehicles detect certain events that may be dangerous to other vehicles and disseminating reports about these events using wireless c ..."
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Abstract — In this paper, we present a platform for aiding in the development and evaluation of novel ITS passive safety applications. Such applications work by having vehicles detect certain events that may be dangerous to other vehicles and disseminating reports about these events using wireless communication. A vehicle receiving the report about the event can then be warned. However, a large number of false warnings will lead to driver desensitization, which will reduce the safety benefit. To overcome this issue, a relevance estimator that will determine for which reports a warning will be given has to be devised for each new application. Our platform allows for an easy, fast method of developing these estimators and evaluating them in simulations. We demonstrated the feasibility of this approach with three example applications.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Estimating Relevance for the Emergency
"... Abstract—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 approac ..."
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Abstract—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. Index Terms—Machine learning, vehicle safety.
A Methodology for the Development of Novel VANET Safety Applications 1
"... ABSTRACT 1 We 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 ..."
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ABSTRACT 1 We 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.
