End-to-End Steering Controller with CNN-based Closed-loop Feedback for Autonomous Vehicles

Jhung, Junekyo; Bae, Il; Moon, Jaeyoung; Kim, Taewoo; Kim, Jin-Cheol; Kim, Shiho

doi:10.1109/ivs.2018.8500440

Cited by 33 publications

(17 citation statements)

References 4 publications

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“…To improve the lateral control in the existing CNN-based steering control systems, Jhung et al [97] have presented the simulated closed-loop feedback-based end-to-end steering control method for the self-driving vehicles. e proposed system is capable of posttraining learning through visual input and repositioning the steering wheels following the predicted steering angle.…”

Section: (3) Neural Network-based Steering Control Techniquesmentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

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Section: (3) Neural Network-based Steering Control Techniquesmentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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“…Gathering additional data from various tracks and environments to train CNN may produce better results. [79] proposed CNN based closed-loop feedback DAVE-2SKY to predict steering wheel angles for lateral control of AVs. The proposed CNN, DAVE-2SKY was compared with traditional CNN-based approaches.…”

Section: A Convolutional Neural Network In Autonomous Vehicle Steerimentioning

confidence: 99%

“…Driving simulators are possibly the best state-of-the-art software of computer-aided kinematic and dynamic simulation and can be regarded as one of the biggest triumphs in the field, as considered by Jia [86]. Although various driving simulators have been developed, in this paper we consider only the driving simulators used in the project we have reviewed; for example, the Car Simulator (CARSIM) [2], the Open Racing Car Simulator (TORCS) [19], [42] Prescan [79], CarND Udacity [39], Gazebo [18], Udacity [83], and Grand Theft Auto V (GTAV) [41]. However, other researchers did not provide information on the driving simulators used in the project.…”

Section: Driving Scenarios For Autonomous Vehicles In Simulated Ementioning

confidence: 99%

“…[77] Torch 7 Scientific computing framework for extensive support within the machine learning framework [2] CARSIM Provides labeled data for the training [19] TORCS 1.3.7 Used as research platform; runs on linux; used for car racing; allows users to develop their own vehicle controllers without human intervention [42] TORCS Used as research platform; runs on linux; used for car racing; human driver controls the vehicle in the simulation and enables the collection of data [79] PreScan Physics-based simulation platform utilized in the automotive industry to develop advanced driver assistance systems based on sensor technologies such as laser/LiDAR, GPS and camera [39] CarND Udacity Flexible software; allows simulation of car movement in manual and automatic mode [18] Gazebo Built using Robot operating system in Ubuntu; safety control module; evaluated performance of tracks in software before real track test [83] Udacity Allows flexible simulation environment [41] Grand Theft Auto V. (GTAV) Action adventure gaming platform for driving [81] Grand Theft Auto (GTA) Video game platform for driving [41] Carla Open source urban driving simulator; provides rich scene and numerous sensor information comprising camera, depth, LIDAR measurements, etc. [77] Operated in diverse road conditions like highways, local and residential roads in rain, sun, fog, day and night with lighting and cloudy conditions [2] Road course, traffic, wind and screen captures [19] Lane markings in tracks mimic similar to real world; not all traffic involved; other cars controlled by the games AI engine created nuisance and instigated crashes [20] Varied widespread driving conditions [42] 40-meter long course with right and left-hand curves and traffic lights [79] Valet parking scenario [39] Modeled on one-hour driving in different styles in manual mode; tried not to collide with objects and not to move out of track; allows modeling styles that can be implemented on special polygons in real world only [18] Salient features: boundaries of tracks and parts of buildings [40] Based on path following [76] Considers straight paths and daytime driving only. [48] Videos captures highway driving in clear weather and daytime; various road types like residential with and without lane markings; .also contains all activities of the driver like staying in and switching lanes.…”

Section: Driving Scenarios For Autonomous Vehicles In Simulated Ementioning

confidence: 99%

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A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

et al. 2020

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Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures have been applied to predict the steering angle of AVs in various scenarios. A survey on steering angle prediction based on deep learning algorithms can help expert researchers identify those areas that require development. Also, novice researchers can use the survey as a starting point. In this paper, we present a broad study on the recent advances made in DL architectures that covers the steering angle prediction of AVs. A new comprehensive taxonomy of the application of DL in steering angle prediction of AVs is created. The survey presents a concise research summary synthesis, and analysis. It is found that most researchers depend on Convolutional Neural Network (CNN) over other DL architectures in predicting the steering angle of autonomous driving vehicles. Also identified are open research problems. The prominent challenge facing DL-based steering angle prediction of AVs is lack of sufficient real-world datasets, which means that researchers largely depend on data generated from simulated environments. Lastly, alternative viewpoints to solve the identified open research challenges are proposed, pointing towards promising future research directions.

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“…Este problemaé conhecido como lane keeping é e um tipo de seguimento de trajetória. Existem diversas formas de solucioná-lo, aplicando técnicas variadas como redes neurais ou algoritmos de tratamento de imagens em conjunto com técnicas de controle (Bing et al, 2018;Jhung et al, 2018;Samuel et al, 2018).…”

Section: Introductionunclassified

Navegação Visual Autônoma de um Veículo Terrestre em Escala Reduzida

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2020

Anais Do Congresso Brasileiro De Automática 2020

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Este artigo propõe e apresenta simulações de um sistema de navegação visual autônoma para um veículo diferencial com quatro rodas em escala reduzida. O sistema de navegação é composto por: 1) um controlador de seguimento de faixa que processa a imagem de uma câmera embarcada no veículo para corrigir sua orientação e velocidade, 2) um algoritmo de localização do veículo que usa filtro de Kalman para estimar a pose do veículo (posição e orientação) usando dados de um receptor GNSS RTK, uma bússola digital e sensores de odometria. Foram investigadas e comparadas três possíveis soluções para o controlador de manutenção de faixa. A primeira solução usa uma abordagem com algoritmo de janelas deslizantes para processar a imagem da câmera embarcada, extrair a distância do veículo para o centro da faixa e o seu raio de curvatura. Um controlador proporcional _e utilizado, então, para calcular a velocidade desejada para as rodas do veículo. A segunda solução usa uma rede neural convolucional profunda para imitar a primeira solução, isso é, a rede neural recebe a imagem da câmera como entrada e é treinada para gerar a mesma saída que o controlador proporcional usado na primeira solução. A terceira solução também aplica uma rede neural convolucional profunda, mas a rede é treinada usando dados gravados quando um motorista humano dirigiu o veículo no mesmo ambiente simulado. Todas as três soluções foram capazes de manter o veículo em uma margem de erro aceitável da trajetória desejada.

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End-to-End Steering Controller with CNN-based Closed-loop Feedback for Autonomous Vehicles

Cited by 33 publications

References 4 publications

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Navegação Visual Autônoma de um Veículo Terrestre em Escala Reduzida

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