A Lane Detection Method for Lane Departure Warning System

He, Jia; Rong, Hui; Gong, Jinfeng; Huang, Wei

doi:10.1109/icoip.2010.307

Cited by 41 publications

(17 citation statements)

References 6 publications

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“…Many lane detection methods in [15], [16], [17], [18] have been developed to locate the lane position with canny edge detection or hough transformation. The defect of these methods is that they lack some geometric constraints to locate the arbitrary lane boundary.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Brain-Inspired Cognitive Model With Attention for Self-Driving Cars

Chen

Zhang

Shang

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

100

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Perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information of autonomous driving process, which are the essential factors for achieving human-like driving into these two methods. In this paper, we propose a novel model for self-driving cars named brain-inspired cognitive model with attention (CMA). This model consists of three parts: a convolutional neural network for simulating human visual cortex, a cognitive map built to describe relationships between objects in complex traffic scene and a recurrent neural network that combines with the realtime updated cognitive map to implement attention mechanism and long-short term memory. The benefit of our model is that can accurately solve three tasks simultaneously: i) detection of the free space and boundaries of the current and adjacent lanes. ii)estimation of obstacle distance and vehicle attitude, and iii) learning of driving behavior and decision making from human driver. More significantly, the proposed model could accept external navigating instructions during an end-to-end driving process. For evaluation, we build a large-scale roadvehicle dataset which contains more than forty thousand labeled road images captured by three cameras on our self-driving car. Moreover, human driving activities and vehicle states are recorded in the meanwhile.Index Terms-autonomous mental development, cognitive robotics, end-to-end learning, path planning, vehicle driving.

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Section: Related Workmentioning

confidence: 99%

“…generate steering angle by RNN 14: generate pedal commands by desired speed 15: else if D m right == D r right ∨ the car is on the boundary of lane then 16: if (D m right − D m light ) ≫ d then 17: the car is on the boundary of lane 18:…”

Section: B Planning and Control Simulating Human Motor Cortexmentioning

confidence: 99%

Brain-Inspired Cognitive Model With Attention for Self-Driving Cars

Chen

Zhang

Shang

et al. 2019

IEEE Trans. Cogn. Dev. Syst.

100

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show abstract

“…In order to reduce the computation time, it is significant to select the ROI [He et al, 2010]. The useless information, such as the portion too far or too near in the image, could be ignored.…”

Section: Horizontal Luminance Differencementioning

confidence: 99%

Lane-Based Direction Marking Recognition Using Hu Moments

Kang

Wei

et al. 2013

Int. J. Inf. Acquisition

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In intelligent vehicle system, it is significant to detect and identify road markings for vehicles to follow traffic regulation. This paper proposes a method to recognize direction markings on road surface, which is on the basis of detected lanes and uses Hu moments. First of all, the detection of lanes is based on horizontal luminance difference, which converts the RGB color image to the luminance image, calculates the horizontal luminance difference, obtains the candidate points of lanes' edge and uses least square method to fit the lanes. Secondly, with the detected lines as guide for the search of candidate marking, the paper extracts Hu moments of candidate marking, calculates its Mahalanobis distance to every marking type and classifies it to the type which has the minimal distance with the candidate marking. From the simulation results, the method to detect lanes is more effective and time-efficient than canny or sobel edge detection methods; the method to recognize direction marking is effective and has a high accuracy.

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“…The edge detection method determines the ROI and edge characteristics or uses scan lines to scan the lane line characteristics within the set ROI range to avoid driving in an offset lane situation under unconscious conditions. The lane line can be divided into two types, namely, straight line 2,3 and curve, 4 indicating that the lane line can use the left and right lane information to determine whether the vehicle may or may not offset to issue a warning.…”

Section: Introductionmentioning

confidence: 99%

Realization of Internet of vehicles technology integrated into an augmented reality system

Pai

Chen

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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The goal of this study is to develop an internet of vehicles system with augmented reality technology. The system deals mainly with three subjects, namely, lane departure warning, forward collision detection and warning, and internet of vehicles. First, to deal with the subject of lane departure warning, the Hough transform is used in this study to extract the possible positions of lane lines from the region of interest of an image. The Kalman filter is further employed to remove noises and estimate the actual positions of car lane lines. The lane departure decision is then used to determine whether a lane departure situation occurs. Second, the Sobel edge detector and taillight detection method are used to locate the hypothetical region of the vehicle. The characteristic parameters within the hypothetical region can also be obtained through the Harris corner detection method. To verify the hypothetical region and identify the vehicle, the support vector machine algorithm is used. The collision decision is then applied to determine whether the distance between two vehicles is short, thus fulfilling the goal of forward collision detection and warning. In addition, a secure and easy-to-use internet of vehicles is achieved with the use of the Rivest-Shamir-Adleman encryption algorithm, which uses public and secret keys to encrypt and decrypt messages to achieve the task of user identification. Upon obtaining control of the vehicle, the driver has full access to the most up-to-date information provided by the driver assistance system. Finally, internet of vehicles applications incorporating the previously mentioned methods, smart glasses, and augmented reality are implemented in this study. Smart glasses provide the drivers easy access to information about the vehicle and warnings, which helps enhance driver convenience and safety considerably.

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A Lane Detection Method for Lane Departure Warning System

Cited by 41 publications

References 6 publications

Brain-Inspired Cognitive Model With Attention for Self-Driving Cars

Brain-Inspired Cognitive Model With Attention for Self-Driving Cars

Lane-Based Direction Marking Recognition Using Hu Moments

Realization of Internet of vehicles technology integrated into an augmented reality system

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