Lane Detection on the iPhone

Ren, Feixiang; Huang, Jinsheng; Terauchi, Mutsuhiro; Jiang, Ruobing; Klette, Reinhard

doi:10.1007/978-3-642-11577-6_25

Cited by 15 publications

(8 citation statements)

References 4 publications

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“…Our lane-keeping algorithm is based on the output of one forward-looking vision sensor, mounted frontally on each vehicle. Similar to [16] which contains image smoothing using Gaussian filter, edge detection using Canny detector, and line detection with the help of Hough transform, our algorithm outputs the angle to the centerline of the lane. The vehicle steering angle φ l is set to match the tangent of the centerline using a PI controller.…”

Section: Lane Keepingmentioning

confidence: 99%

Distributed graph-based convoy control for networked intelligent vehicles

Marjovi

Vasić

Lemaitre

et al. 2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopardizing the ensemble's stability. Additionally, we integrate two essential control behaviors for lane-keeping and obstacle avoidance into the controller. To increase the performance of the convoy controller in terms of formation maintenance and fuel economy, the parameters of the controller are optimized in realistic scenarios using Particle Swarm Optimization (PSO), a powerful metaheuristic optimization method well-suited for large parameter spaces. The performances of the optimized controllers are evaluated in high-fidelity multi-vehicle simulations outlining the efficiency and robustness of the proposed strategy.

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Section: Lane Keepingmentioning

confidence: 99%

Distributed graph-based convoy control for networked intelligent vehicles

Marjovi

Vasić

Lemaitre

et al. 2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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“…Computer vision techniques have been proposed to detect the car lane. For example, in [10] authors use the iPhone camera to detect the lane markings. Using cameras for lane detection, however, is highly susceptible to errors due to various factors such as lighting condition (e.g.…”

Section: A Lane Determinationmentioning

confidence: 99%

“…On the other hand, computer vision based techniques, e.g. [10], use a camera to detect the lane markings. However, using an image processing solution raises accuracy challenges when road markings are unclear, line-of-sight is obstructed, and/or in bad weather conditions.…”

Section: Introductionmentioning

confidence: 99%

LaneQuest: An accurate and energy-efficient lane detection system

Aly¹,

Basalamah

Youssef

2015

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane "anchors", that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors.Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.

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“…As discussed below, they integrate several general purpose cores, graphics processing units and other co-processors on a single chip keeping power consumption very low. The applications they can address are increasingly complex (Taylor et al, 2009) (Wither et al, 2011) (Ren et al, 2010).…”

Section: Microprocessorsmentioning

confidence: 99%