100,443 research outputs found
Characterizing driving behavior using automatic visual analysis
In this work, we present the problem of rash driving detection algorithm
using a single wide angle camera sensor, particularly useful in the Indian
context. To our knowledge this rash driving problem has not been addressed
using Image processing techniques (existing works use other sensors such as
accelerometer). Car Image processing literature, though rich and mature, does
not address the rash driving problem. In this work-in-progress paper, we
present the need to address this problem, our approach and our future plans to
build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201
âEyes freeâ in-car assistance: parent and child passenger collaboration during phone calls
This paper examines routine family car journeys, looking specifically at how passengers assist during a mobile telephone call while the drivers address the competing demands of handling the vehicle, interacting with various artefacts and controls in the cabin, and engage in co-located and remote conversations while navigating through busy city roads. Based on an analysis of video fragments, we see how drivers and child passengers form their conversations and requests around the call so as to be meaningful and paced to the demands, knowledge and abilities of their cooccupants, and how the conditions of the road and emergent traffic are oriented to and negotiated in the context of the social interaction that they exist alongside. The study provides implications for the design of car-based collaborative media and considers how hands- and eyesfree natural interfaces could be tailored to the complexity of activities in the car and on the road
Smart driving assistance systems : designing and evaluating ecological and conventional displays
In-vehicle information systems have been shown to increase driver workload and cause distraction;
both are causal factors for accidents. This simulator study evaluates the impact that two designs for
a smart driving aid and scenario complexity has on workload, distraction and driving performance.
Results showed that real-time delivery of smart driving information did not increase driver workload
or adversely affect driver distraction, while having the effect of decreasing mean driving speed
in both the simple and complex driving scenarios. Important differences were also highlighted
between conventional and ecologically designed smart driving interfaces with respect to subjective
workload and peripheral detection
Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web
Current âInternet of Thingsâ concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3Câs Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where driversâ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun
Ontology based Scene Creation for the Development of Automated Vehicles
The introduction of automated vehicles without permanent human supervision
demands a functional system description, including functional system boundaries
and a comprehensive safety analysis. These inputs to the technical development
can be identified and analyzed by a scenario-based approach. Furthermore, to
establish an economical test and release process, a large number of scenarios
must be identified to obtain meaningful test results. Experts are doing well to
identify scenarios that are difficult to handle or unlikely to happen. However,
experts are unlikely to identify all scenarios possible based on the knowledge
they have on hand. Expert knowledge modeled for computer aided processing may
help for the purpose of providing a wide range of scenarios. This contribution
reviews ontologies as knowledge-based systems in the field of automated
vehicles, and proposes a generation of traffic scenes in natural language as a
basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10
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