An effective lane-detection algorithm is a fundamental component of an advanced driver assistant system, as it provides important information that supports driving safety. The challenges faced by the lane detection and tracking algorithm include the lack of clarity of lane markings, poor visibility due to bad weather, illumination and light reflection, shadows, and dense road-based instructions. In this paper, a robust and real-time vision-based lane detection algorithm with an efficient region of interest is proposed to reduce the high noise level and the calculation time. The proposed algorithm also processes a gradient cue and a color cue together and a line clustering with scan-line tests to verify the characteristics of the lane markings. It removes any false lane markings and tracks the real lane markings using the accumulated statistical data. The experiment results show that the proposed algorithm gives accurate results and fulfills the real-time operation requirement on embedded systems with low computing power.

Robust Lane Detection and Tracking for Real-Time Applications

A review of lane detection methods based on deep learning

To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v3 (You Only Look Once, v3) is constructed. The structural parameters of the YOLO v3 algorithm are modified to make it more suitable for lane detection. To improve the training efficiency, a method for automatic generation of the lane label images in a simple scenario, which provides label data for the training of the first-stage network, is proposed. Then, an adaptive edge detection algorithm based on the Canny operator is used to relocate the lane detected by the first-stage model. Furthermore, the unrecognized lanes are shielded to avoid interference in subsequent model training. Then, the images processed by the above method are used as label data for the training of the second-stage model. The experiment was carried out on the KITTI and Caltech datasets, and the results showed that the accuracy and speed of the second-stage model reached a high level.

https://www.mdpi.com/1424-8220/18/12/4308/pdf

A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3.

Curve is the traffic accident-prone area in the traffic system of the structural road. How to effectively detect the lane-line and timely give the traffic information ahead for drivers is a difficult point for the assisted safe driving. The traditional lane detection technology is not very applicable in the curved road conditions. Thus, a curve detection algorithm which is based on straight-curve model is proposed in this paper and this method has good applicability for most curve road conditions. First, the method divides the road image into the region of interest and the road background region by analyzing the basic characteristics of the road image. The region of interest is further divided into the straight region and the curve region. At the same time, the straight-curve mathematical model is established. The mathematical equation of the straight model is obtained by using the improved Hough transform. The polynomial curve model is established according to the continuity of the road lane-line and the tangent relationship between the straight model and the curve model. Then, the parameters of the curve model equation are solved by the curve fitting method. Finally, the detection and identification of the straight and the curve are realized respectively and the road lane-line is reconstructed. Experiments show that this method can accurately identify the curve lane-line, provide effective traffic information, make early warning, and it also has certain universality.

Lane Detection of Curving Road for Structural Highway With Straight-Curve Model on Vision

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor.

Road conditions can provide important information for driving safety in driving assistance system. The input images usually include unnecessary information and road conditions need to be analyzed only in a region of interest (ROI) to reduce the amount of computation. In this paper, a vision-based road ROI determination algorithm is proposed to detect the road region using the positional information of a vanishing point and line segments. The line segments are detected using Hough Transform. The road ROI can be determined automatically and adaptively in every frame. The proposed method is applied to various video images from black boxes, and is verified to be robust.

An adaptive road ROI determination algorithm for lane detection

A new Viterbi decoder is proposed using a modified register exchange scheme. The trace-back operation is eliminated in the new architecture, and the amount of memory is reduced. The elimination of the trace-back operation also reduces the operation cycles to determine decision bits. The Viterbi decoder has low latency, efficient memory organization, and low hardware complexity compared with other Viterbi decoding methods in block decoding architectures. The proposed decoder can be designed with emphasis on either efficient memory or low latency.

High performance Viterbi decoder using modified register exchange methods

In this paper, we implemented the conventional FAST and BRIEF algorithm as hardware on Zynq-7000 SoC Platform. Previous feature-based hardware accelerator is mostly implemented using the SIFT or SURF algorithm, but it requires excessive internal memory and hardware cost. The proposed FAST & BRIEF accelerator reduces approximately 57% of internal memory usage and 70% of hardware cost compared to the conventional SIFT or SURF accelerator, and it processes 0.17 pixel per clock.

FPGA based implementation of FAST and BRIEF algorithm for object recognition

This paper presents a novel silicon controlled rectifier (SCR)-based (Electrostatic Discharge) ESD protection devices for I/O clamp and power clamp. The proposed ESD protection devices has a high holding voltage and a low tigger voltage characteristic than conventional SCR. These characteristics enable to latch-up immune under normal operating conditions as well as superior full chip ESD protection. Also, the propsed devices can provide area efficiency in comparison to conventional (Gate Grounded NMOS) GGNMOS. The propsed devices are fabricated by using 0.35um BCD (Bipolar-CMOS-DMOS) technology. From the experimental results, the device for Input/Output (I/O) clamp has a trigger voltage of 6.5V, 7.7V and 8.1V with the LG1 of 0.5um, 0.8um and 1um, respectively. And the device for power clamp has a holding voltage of 8V, 10V and 11.3V with the D1 of 4.5um, 5.5um and 7um. Also, the device for I/O clamp has trigger voltage of 7.8V to 8.9V with the gate length (LG1) of 0.5um, 0.8um and 1.0um. Moreover, The proposed devices have high ESD robustness.

Electrical characteristics of novel ESD protection devices for I/O and power clamp

This thesis is intended to design multi thread mobile GPGPU optimized in mobile environment, and to verify an effective thread management method of the multi thread mobile processor. For the verification of the multi thread management method, Lane detection algorithm was implemented to compare nVidia's CUDA Architecture and the designed GPGPU in terms of thread management efficiency. The number of thread is normalized to 48 threads. An implemented Lane Detection Algorithm is composed of Gaussian filter algorithm and Sobel Edge Detection algorithm. As a result, the designed GPGPU's thread efficiency is up to 2 times higher than CUDA's thread efficiency.

Chanho Lee

Papers

An adaptive road ROI determination algorithm for lane detection

High performance Viterbi decoder using modified register exchange methods

FPGA based implementation of FAST and BRIEF algorithm for object recognition

Electrical characteristics of novel ESD protection devices for I/O and power clamp

Implementation of multi thread management system on mobile GPGPU