Visual lane analysis and higher-order tasks: a concise review

Shin, Bok-Suk; Xu, Zezhong; Klette, Reinhard

doi:10.1007/s00138-014-0611-8

Cited by 40 publications

(22 citation statements)

References 107 publications

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“…One of the first works trying to fuse “out” information (visual lane analysis) and “in” information (driver monitoring) is the one proposed by Apostoloff et al [271], pointing out the benefits of this approach. Indeed, visual lane analysis can be used for “higher-order tasks”, which are defined by interacting with other modules in a complex driver assistance system (e.g., understanding the driver’s attentiveness—distraction—to the lane-keeping task [272]). Hirayama et al [273] focused on temporal relationships between the driver’s eye gaze and the peripheral vehicles behaviour.…”

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

104

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

104

View full text Add to dashboard Cite

show abstract

“…Additional knowledge such as the width of the car or the previous trajectory of the car can be used for estimating the continuation of lanes. Authors of [26] also write: "The localisation of a lane is not always uniquely defined in the real world; it may depend on traffic flow or driving comfort if there is no unique lane marking." Compared methods: We compare two methods of lane detection for which we have all the sources available, the previously published particle-filter-based lane detector of [15] (called Method 1) and our novel superparticle-based detector (called Method 2).…”

Section: Comparative Performance Evaluationmentioning

confidence: 97%

“…Current research also considers the subject of lane detection as an integral part of higher-order tasks, e.g. combined with components such as curb detection, traffic sign recognition, vehicle tracking and visual navigation [26].…”

Section: Introductionmentioning

confidence: 99%

“…They have been designed and implemented for situations defined by good viewing conditions and clear lane markings on highways. Since those early days in the 1990s, visual lane analysis remains an ongoing research subject [5,17,23,26], with a focus on particular subjects such as accuracy for selected challenging conditions, robustness for a wide range of scenarios, time efficiency, or integration into higher-order tasks.…”

Section: Introductionmentioning

confidence: 99%

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A superparticle filter for lane detection

Shin

Tao

Klette

2015

Pattern Recognition

Self Cite

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“…The free space is a region ahead of the ego-vehicle where this vehicle may potentially (i.e. safely) drive in, for example, in the next few seconds [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

Saleem

Chien

Rezaei

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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This paper highlights the role of ground manifold modelling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different groundmanifold models (e.g. plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. The paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results compared to binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. Experimental results also include quantitative evaluations of the trade-off between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions.

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Visual lane analysis and higher-order tasks: a concise review

Cited by 40 publications

References 107 publications

Driver Distraction Using Visual-Based Sensors and Algorithms

Driver Distraction Using Visual-Based Sensors and Algorithms

A superparticle filter for lane detection

Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

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