A new lane following method based on deep learning for automated vehicles using surround view images

Han, Kyung Yeop; Yu, Jihun; Lee, Young-Sup

doi:10.1007/s12652-019-01496-8

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…In deep learning, inputs are taken from images, text, and sound and segregated. ese models have more accuracy (sometimes more than humans) [175]. Models are trained by using multiple layers of input data.…”

Section: Deep Learning and Deep Reinforcement Learningmentioning

confidence: 99%

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Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Bathla

Bhadane²,

Singh

et al. 2022

Mobile Information Systems

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Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.

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Section: Deep Learning and Deep Reinforcement Learningmentioning

confidence: 99%

“…e deep learning neural network is more beneficial over the conventional machine learning technique [175]. According to Lee et al, deep learning techniques are used for autonomous vehicles in following a lane without taking many lane departures.…”

Section: Deep Learning and Deep Reinforcement Learningmentioning

confidence: 99%

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Bathla

Bhadane²,

Singh

et al. 2022

Mobile Information Systems

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“…Authors in Lee et al (2019) proposed a method based on deep learning from surround view images for autonomous driving of a ground vehicle evaluated on various unfavorable conditions with high-curvature lanes. In Wang et al (2008), the authors use a hyperbola model for marking detection.…”

Section: Curved Lanesmentioning

confidence: 99%

Bayesian curved lane estimation for autonomous driving

Fakhfakh

Chaâri

Fakhfakh

2020

J Ambient Intell Human Comput

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Several pieces of research during the last decade in intelligent perception are focused on the development of algorithms allowing vehicles to move efficiently in complex environments. Most of existing approaches suffer from either processing time which do not meet real-time requirements, or inefficient in real complex environment, which also doesn't meet the full availability constraint of such a critical function. To improve the existing solutions, an algorithm based on curved lane detection by using a Bayesian framework for the estimation of multi-hyperbola parameters is proposed to detect curved lane under challenging conditions. The general idea is to divide a captured image into several parts. The trajectory is modeled by a hyperbola over each part, whose parameters are estimated using the proposed hierarchical Bayesian model. Compared to the existing works in the state of the art, experimental results prove that our approach is more efficient and more precise in road marking detection. Keywords Autonomous driving • embedded camera • road marking • multi-hyperbola • Bayesian framework

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“…Lane following is one of the most important autonomous driving subsystems. Only after successfully implementing the lane following function can other advanced subsystems of autonomous driving such as obstacle avoidance and car following be further developed [ 1 ]. The existing lane following algorithm only considers the lateral motion of the vehicle, and rarely considers the influence of the longitudinal dynamics of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Lane Following Method Based on Improved DDPG Algorithm

Zhang

et al. 2021

Sensors

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In an autonomous vehicle, the lane following algorithm is an important component, which is a basic function of autonomous driving. However, the existing lane following system has a few shortcomings: first, the control method it adopts requires an accurate system model, and different vehicles have different parameters, which needs a lot of parameter calibration work. The second is that it may fail on road sections where the lateral acceleration requirements of vehicles are large, such as large curves. Third, its decision-making system is defined based on rules, which has disadvantages: it is difficult to formulate; human subjective factors cannot guarantee objectivity; coverage is difficult to guarantee. In recent years, the deep deterministic policy gradient (DDPG) algorithm has been widely used in the field of autonomous driving due to its strong nonlinear fitting ability and generalization performance. However, the DDPG algorithm has overestimated state action values and large cumulative errors, low training efficiency and other issues. Therefore, this paper improves the DDPG algorithm based on the double critic networks and priority experience replay mechanism. Then this paper proposes a lane following method based on this algorithm. Experiment shows that the algorithm can achieve excellent following results under various road conditions.

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A new lane following method based on deep learning for automated vehicles using surround view images

Cited by 8 publications

References 33 publications

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Bayesian curved lane estimation for autonomous driving

Lane Following Method Based on Improved DDPG Algorithm

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