Vision-Based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization

Satzoda, Ravi Kumar; Trivedi, Mohan M.

doi:10.1109/cvprw.2013.91

Cited by 35 publications

(24 citation statements)

References 13 publications

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“…In order to detect the lane related events listed in Section III, the lane estimation algorithm described in [21], [22] called LASeR (lane analysis using selective regions) is employed. For the sake of completeness of this paper, LASeR is briefly described in this sub-section.…”

Section: B Lane Estimation Algorithmmentioning

confidence: 99%

Drive analysis using lane semantics for data reduction in naturalistic driving studies

Satzoda

Gunaratne²,

Trivedi

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-Naturalistic driving studies (NDS) provide critical information about driving behaviors and characteristics that could lead to crashes and near-crashes. Such studies involve analysis of large volumes of data from multiple sensors and detection and extraction of critical events is an important step in NDS. This paper introduces techniques that analyze the visual data complemented with other sensors in the vehicle to determine critical events related to lane drifts, road departures and road delineations. To the best knowledge of the authors, this is the first work that detects and extract events listed in visual reference dictionary of NDS studies like Strategic Highway Research Program 2 (SHRP2). Detailed evaluations with real-world NDS data is presented.

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Section: B Lane Estimation Algorithmmentioning

confidence: 99%

Drive analysis using lane semantics for data reduction in naturalistic driving studies

Satzoda

Gunaratne²,

Trivedi

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…Lane detection and tracking can be used to find the ego-vehicle position with respect to the lane markings [12], [13]. We employ VioLET lane estimation and tracking method as described in [11], which employs the steps shown in Fig.…”

Section: A Lane Change Analysismentioning

confidence: 99%

Towards automated drive analysis: A multimodal synergistic approach

Satzoda

Martin

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Naturalistic driving studies (NDS) capture huge amounts of drive data, that is analyzed for critical information about driver behavior, driving characteristics etc. Moreover, NDS involve data collected from a wide range of sensing technologies in cars and this makes the analysis of this data a challenging task. In this paper, we propose a multimodal synergistic approach for automated drive analysis process that can be employed in analyzing large amounts of drive data. The visual information from cameras, vehicle dynamics from CAN bus, vehicle global positioning coordinates from GPS and digital road map data, that are collected during the drive, are analyzed in a collaborative and complementary manner in the approach presented in this paper. It will be shown that the proposed synergistic drive analysis approach automatically determines a wide range of critical information about the drive in varying road conditions.

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“…This topic can be separated into three essential blocks: lane marking detection, lane tracking and lane departure warning. For the lane marking detection algorithm, the existing solutions can be classified as feature-based [1,3,4], model-based [2,5] and region-based [1,6] methods. Among these methods, different feature-based methods for Lane detection are still the most popular and commonly used ways which contribute to its efficiency in multiple algorithms.…”

Section: Introductionmentioning

confidence: 99%

Robust and Real-Time Lane Marking Detection for Embedded System

Guo

Zhang

et al. 2015

Lecture Notes in Computer Science

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Abstract. Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle's departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm.

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Vision-Based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization

Abstract: Abstract

Cited by 35 publications

References 13 publications

Drive analysis using lane semantics for data reduction in naturalistic driving studies

Drive analysis using lane semantics for data reduction in naturalistic driving studies

Towards automated drive analysis: A multimodal synergistic approach

Robust and Real-Time Lane Marking Detection for Embedded System

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