Camera-Based Lane Marking Detection for ADAS and Autonomous Driving

Alkhorshid, Yasamin; Aryafar, Kamelia; Wanielik, Gerd; Shokoufandeh, Ali

doi:10.1007/978-3-319-20801-5_57

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Though GPS has excellent localization performance in open grounds, it is not reliable in GPS-denied areas such as tunnels because pose error increases and a signal blocking problem occurs when satellite signals are not received directly into the rover system. To avoid the aforementioned problem, different methods have been proposed to efficiently localize the vehicle, such as camera-based lane detection [ 26 , 27 ], LiDAR-based point cloud map (PCM) matching [ 28 ], and IMU odometry [ 29 ]. Clothoid uses the combination of sensors i.e., GPS, IMU, and LiDAR for better estimation of vehicle pose and performs the localization by using PCM-based pose estimation and IMU odometry.…”

Section: Localizationmentioning

confidence: 99%

Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Arshad

Sualeh

Kim

et al. 2020

Sensors

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In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

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Section: Localizationmentioning

confidence: 99%

Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Arshad

Sualeh

Kim

et al. 2020

Sensors

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“…Urbanization introduces multi-path errors, which may deteriorate the quality of localization. Lane-level localization using visual cues such as lane markings or other road signs is also possible (e.g., Li et al, 2010;Alkhorshid et al, 2015;Gaoya;Kamijo et al, 2015;Qu et al, 2015;Cao et al, 2016;Kim et al, 2017). But such systems can suffer inaccuracies due to occlusion.…”

Section: Introductionmentioning

confidence: 99%

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

Sharath

Mehran

2021

Front. Future Transp.

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The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

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“…Among all these sensors, camera has been the most affordable sensor which is suitable for detection of different types of objects such as pedestrians, other vehicles on the road or lane marking detection. Due to the existence of many potential applications, employing visual perception for semantic understanding of the road scenes has been widely studied in the industry and academia [5]. On the other hand, the use of camera as the primary sensory input is entangled with the high complexity due to many active disturbances and noise and the variety of moving objects surrounding as well as ego-vehicle movement during the processing.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 illustrates a sample lane marking detection and road detection approach. Detection and tracking of lane marking is essential for driving safety and intelligent vehicle [5], [6]. Offline road understanding and Lane detection algorithms are generally composed of multiple modules: image preprocessing, feature extraction and model-fitting [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Road Detection through Supervised Classification

Alkhorshid

Aryafar

Bauer

et al. 2016

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, lidar, GNSS, vehicle odometry, and computer vision. This sensory input provides a rich dataset that can be used in combination with machine learning models to tackle multiple problems in supervised settings. In this paper we focus on road detection through gray-scale images as the sole sensory input. Our contributions are twofold: first, we introduce an annotated dataset of urban roads for machine learning tasks; second, we introduce a road detection framework on this dataset through supervised classification and hand-crafted feature vectors.

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Camera-Based Lane Marking Detection for ADAS and Autonomous Driving

Cited by 6 publications

References 12 publications

Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

Road Detection through Supervised Classification

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