Vehicle Iconic Surround Observer: Visualization platform for intelligent driver support applications

Morris, Brendan; Trivedi, Mohan M.

doi:10.1109/ivs.2010.5547959

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…As research advances in the area of holistic sensing for driver safety and assistance systems, we will continue to see a growing fusion of technologies including omnidirectional video processing [25], [26], speech recognition [13], driver behavior analysis [10], [11], [12], and object detection [27], [28]. We believe the smartphone platform is a valuable addition to a holistic DAS, not only because of its advanced sensors and ability to recognize driving style, but also because of its access to global networks via its alwayson internet connection.…”

Section: Discussionmentioning

confidence: 99%

Driving style recognition using a smartphone as a sensor platform

Johnson

Trivedi

2011

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

598

310

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Abstract-Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentiallyaggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.

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Section: Discussionmentioning

confidence: 99%

Driving style recognition using a smartphone as a sensor platform

Johnson

Trivedi

2011

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

598

310

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“…As described in [1]- [3], it is crucial to not only consider the car's surrounding and external environment when designing an assist system but also consider the internal environment and take the driver into account. Fusing other types of information with the sign detector, as described in [4], can make the overall system even better.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey

Møgelmose

Trivedi

Moeslund

2012

IEEE Trans. Intell. Transport. Syst.

570

289

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“…Future work will include testing in different settings, such as urban lane changes and highway lane changes. We will also explore HMI concepts and interactivity with the driver [21].…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

Merge recommendations for driver assistance: A cross-modal, cost-sensitive approach

Sivaraman

Trivedi

Tippelhofer

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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In this study, we present novel work focused on assisting the driver during merge maneuvers. We use an automotive testbed instrumented with sensors for monitoring critical regions in the vehicle's surround. Fusing information from multiple sensor modalities,we integrate measurements into a contextually relevant, intuitive, general representation, which we term the Dynamic Probabilistic Drivability Map [DPDM]. We formulate the DPDM for driver assistance as a compact representation of the surround environment, integrating vehicle tracking information, lane information, road geometry, obstacle detection, and ego-vehicle dynamics. Given a robust understanding of the ego-vehicle's dynamics, other vehicles, and the on-road environment, our system recommends merge maneuvers to the driver, formulating the maneuver as a dynamic programming problem over the DPDM, searching for the minimum cost solution for merging. Based on the configuration of the road, lanes, and other vehicles on the road, the system recommends the appropriate acceleration or deceleration for merging into the adjacent lane, specifying when and how to merge.

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Vehicle Iconic Surround Observer: Visualization platform for intelligent driver support applications

Cited by 13 publications

References 15 publications

Driving style recognition using a smartphone as a sensor platform

Driving style recognition using a smartphone as a sensor platform

Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey

Merge recommendations for driver assistance: A cross-modal, cost-sensitive approach

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