Real-Time Self-Driving Car Navigation Using Deep Neural Network

Do, Truong-Dong; Duong, Minh-Thien; Dang, Quoc-Vu; Le, My-Ha

doi:10.1109/gtsd.2018.8595590

Cited by 90 publications

(49 citation statements)

References 6 publications

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“…Convolutional neural network (CNN) is a DL architecture that comprises multiple layers [2], [20]. CNN is the most attractive DL architecture in use due to its efficiency in solving image processing problems.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

“…However, the amount of training data was small and did not cover many scenarios. [20] proposed CNN for steering angles of an autonomous vehicle. CNN was trained using data collected from the vehicle platform that was built with 1/10 scale RC car, Raspberry Pi 3 model B computer.…”

Section: A Convolutional Neural Network In Autonomous Vehicle Steerimentioning

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%

“…Many studies have applied DL in predicting the steering angle of AVs [2], [18], [19], [7], [20]. Kuutti, Bowden [4] and Oussama and Mohamed [21] surveyed DL applications in AV control.…”

Section: Introductionmentioning

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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Section: A Convolutional Neural Networkmentioning

confidence: 99%

Section: A Convolutional Neural Network In Autonomous Vehicle Steerimentioning

confidence: 99%

Section: Driving Scenarios For Autonomous Vehicles In Simulated Ementioning

confidence: 99%

“…Many studies have applied DL in predicting the steering angle of AVs [2], [18], [19], [7], [20]. Kuutti, Bowden [4] and Oussama and Mohamed [21] surveyed DL applications in AV control.…”

Section: Introductionmentioning

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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“…Farabet et al [26] introduce a multi-scale convolutional network for scene labeling and segmentation of an image, which contains multiple copies of a single network that encodes information from raw pixels separately and then combines the information to represent shape, texture, and contextual features of the image. For real time autonomous driving, CNNs have shown significant performance on analyzing images related to traffic signals and vehicle movement [24,40]. For processing 3D models, CNNs also outperform other architectures in terms of accuracy and simplicity.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Semantics-Guided Exploration of Latent Spaces for Shape Synthesis

Jahan¹

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We introduce an approach to incorporate user guidance into shape synthesis approaches based on deep networks. Synthesis networks such as auto-encoders are trained to encode shapes into latent vectors, effectively learning a latent shape space that can be sampled for generating new shapes. Our main idea is to allow users to start an exploratory process of the shape space with the use of high-level semantic keywords. Specifically, the user inputs a set of keywords that describe the general attributes of the shape to be generated, e.g., "four legs" for a chair. Next, we map the keywords to a subspace of the latent space that captures the shapes possessing the specified attributes. The user then explores only the subspace to search for shapes that satisfy the design goal, in a process similar to using a parametric shape model. Our exploratory approach allows users to model shapes at a high-level without the need of advanced artistic skills, in contrast to existing methods that allow to guide the synthesis with sketching or partial modeling of a shape. Our technical contribution to enable this exploration-based approach is the introduction of a label regression neural network coupled with a shape synthesis neural network. The label regression network takes the user-provided keywords and maps them to the corresponding subspace of the latent space, where the subspace is modeled as a set of distributions for the dimensions of the latent space. We show that our method allows users to efficiently explore the shape space and generate a variety of shapes with selected high-level attributes. iii I would also like to thank my defense committee Dr. David Mould, Dr. Yongyi Mao and Dr. Matthew Holden for their valuable and useful critiques towards the thesis. Special thanks to my fellow lab member Yanran Guan, who helped me in labeling the training dataset for this research. My sincere thanks goes to all members of GIGL lab for their consistent motivation and support in the past two years. I am also grateful to the School of Computer Science, Carleton University for accepting me as a research student and facilitating me with financial assistance to complete my degree. At last, I would like to thank my parents, sister, family members and my beloved husband for their unconditional love and support that encouraged me to complete my research.

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