Lane marking based vehicle localization using particle filter and multi-kernel estimation

Lu, Wenjie; Emmanuel, Sabu; Rodriguez, F. Sergio A.; Reynaud, Roger

doi:10.1109/icarcv.2014.7064372

Cited by 26 publications

(18 citation statements)

References 19 publications

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“…As summarized in the survey paper by Hillel et al in [68], most of the lane line detection algorithms share three common steps: (1) lane line feature extraction, by edge detection [76,77] and color [78,79], by learning algorithms such as SVM [80], or by boost classification [81,82]; (2) fitting the pixels into different models, e.g., straight lines [83,84], parabolas [85,86], hyperbolas [87][88][89], and even zigzag line [90]; (3) estimating the vehicle pose based on the fitted model. A fourth time integration step may exist before the vehicle pose estimation in order to impose temporal continuity, where the detection result in the current frame is used to guide the next search through filter mechanisms, such as Kalman filter [76,91] and particle filter [80,90,92].…”

Section: Lane Line Marking Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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Section: Lane Line Marking Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…Lane markings are the most popularly used symbols for vehicle localization. Nedevschi et al [19], Jo et al [20], Lu et al [21], Gruyer et al [22], Tao et al [23], Shunsuke et al [24], and Suhr et al [2] utilize a variety of types of cameras to detect lane markings for vehicle localization purposes. In particular, Nedevschi et al [19] not only recognize positions of lanes but also their types (e.g., double, single, interrupted, and merge) as additional information.…”

Section: Related Researchmentioning

confidence: 99%

Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways

Jang

Suhr

Jung

2018

Sensors

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Landmark-based vehicle localization is a key component of both autonomous driving and advanced driver assistance systems (ADAS). Previously used landmarks in highways such as lane markings lack information on longitudinal positions. To address this problem, lane endpoints can be used as landmarks. This paper proposes two essential components when using lane endpoints as landmarks: lane endpoint detection and its accuracy evaluation. First, it proposes a method to efficiently detect lane endpoints using a monocular forward-looking camera, which is the most widely installed perception sensor. Lane endpoints are detected with a small amount of computation based on the following steps: lane detection, lane endpoint candidate generation, and lane endpoint candidate verification. Second, it proposes a method to reliably measure the position accuracy of the lane endpoints detected from images taken while the camera is moving at high speed. A camera is installed with a mobile mapping system (MMS) in a vehicle, and the position accuracy of the lane endpoints detected by the camera is measured by comparing their positions with ground truths obtained by the MMS. In the experiment, the proposed methods were evaluated and compared with previous methods based on a dataset acquired while driving on 80 km of highway in both daytime and nighttime.

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“…The price of vision sensor is low but the information from them are helpful for estimating the vehicle position. The lane marking detection for localization is one of the research that can improve the accuracy of the localization (Lu et al, 2014). Visual odometry approach can * Corresponding author compute the relative poses in the sequential images in real time (Nister et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Localization of a Car Based on Multi-Sensor Fusion

Kim

Lee

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The vehicle localization is an essential component for stable autonomous car operation. There are many algorithms for the vehicle localization. However, it still needs much improvement in terms of its accuracy and cost. In this paper, sensor fusion based localization algorithm is used for solving this problem. Our sensor system is composed of in-vehicle sensors, GPS and vision sensors. The localization algorithm is based on extended Kalman filter and it has time update step and measurement update step. In the time update step, in-vehicle sensors are used such as yaw-rate and speed sensor. And GPS and vision sensor information are used to update the vehicle position in the measurement update step.We use visual odometry library to process vision sensor data and generate the moving distance and direction of the car. Especially, when performing visual odometry we use georeferenced image database to reduce the error accumulation. Through the experiments, the proposed localization algorithm is verified and evaluated. The RMS errors of the estimated result from the proposed algorithm are about 4.3m. This result shows about 40% improvement in accuracy even in comparison with the result from the GPS only method. It shows the possibility to use proposed localization algorithm. However, it is still necessary to improve the accuracy for applying this algorithm to the autonomous car. Therefore, we plan to use multiple cameras (rear cameras or AVM cameras) and more information such as high-definition map or V2X communication. And the filter and error modelling also need to be changed for the better results.

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Lane marking based vehicle localization using particle filter and multi-kernel estimation

Cited by 26 publications

References 19 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways

Localization of a Car Based on Multi-Sensor Fusion

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