Improved vision-based lane tracker performance using vehicle localization

Sivaraman, Sayanan; Trivedi, Mohan M.

doi:10.1109/ivs.2010.5547967

Cited by 22 publications

(16 citation statements)

References 25 publications

(73 reference statements)

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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)

596

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)

596

310

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“…Therefore, points of the real world belonging to the road, with world coordinates w P are projected into the image at coordinates i P through the projection matrix T M as in (8).…”

Section: Lane Marker Detection Algorithmmentioning

confidence: 99%

“…Lane marker detection [1,5,7,8,10,17] has received considerable attention in driving assistance system. Techniques used varied from using low level morphological operations [1] to probabilistic grouping and B-snakes [2,6,9] , using monocular [1] to stereo vision [3,4] .…”

Section: Introductionmentioning

confidence: 99%

The Research of Lane Marker Detection Algorithm Based on Inverse Perspective Mapping

Xu¹,

Cheng²,

Wan³

2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

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Abstract. This paper presents a robust and fast algorithm for lane marker detection. It is based on generating an inverse perspective mapping image of the road, computing line abstracted regions and the class points from IPM image, which is followed by a post processing step using rectangular approximation to detect lane marker. A reference database containing over 100 images of natural road scenes was built with corresponding manually labeled ground truth images (available at http://www.lcpc.fr/english/products/). This database enables to evaluate lane marker detection algorithm in a systematic way. The experiment proves that the algorithm proposed can detect the lane marker on the road in various conditions.

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“…Vehicle detection and tracking has been widely explored in the literature in recent years [15], [28]- [30]. In [13], a variety of features were used for vehicle detection, including rectangular features and Gabor filter responses.…”

Section: B Vehicle Detection and Trackingmentioning

confidence: 99%

Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

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163

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Abstract-In this paper, we introduce a synergistic approach to integrated lane and vehicle tracking for driver assistance. The approach presented in this paper results in a final system that improves on the performance of both lane tracking and vehicle tracking modules. Further, the presented approach introduces a novel approach to localizing and tracking other vehicles on the road with respect to lane position, which provides information on higher contextual relevance that neither the lane tracker nor vehicle tracker can provide by itself. Improvements in lane tracking and vehicle tracking have been extensively quantified. Integrated system performance has been validated on real-world highway data. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11 frames per second.

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Improved vision-based lane tracker performance using vehicle localization

Cited by 22 publications

References 25 publications

Driving style recognition using a smartphone as a sensor platform

Driving style recognition using a smartphone as a sensor platform

The Research of Lane Marker Detection Algorithm Based on Inverse Perspective Mapping

Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

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