Abstract: Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. 2 Deep Learning based Autonomous driving is a popular and promising field in artificial intelligence. In this survey, we review recent visual-based lane detection datasets and methods. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). The authors are with Elektrobit Automotive and the Robotics, Vision and Control Laboratory (ROVIS Lab) at the Department of Automation and Information Technology, Transilvania University of Brasov, 500036 Brasov, Romania. Dependable Neural Networks for Safety Critical Tasks. The full text of this article hosted at iucr.org is unavailable due to technical difficulties. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Learn more. The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The machine learning community has been overwhelmed by a plethora of deep learning--based approaches. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. AnnotatorJ: an ImageJ plugin to ease hand annotation of cellular compartments. Although lane detection is challenging especially with complex road conditions, considerable progress has been witnessed in this area in the past several years. Working off-campus? Why is Internet of Autonomous Vehicles not as Plug and Play as We Think ? Unlimited viewing of the article PDF and any associated supplements and figures. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. A Survey of Deep Learning Techniques for Autonomous Driving @article{Grigorescu2020ASO, title={A Survey of Deep Learning Techniques for Autonomous Driving}, author={S. Grigorescu and Bogdan Trasnea and Tiberiu T. Cocias and Gigel Macesanu}, journal={J. Engineering Dependable and Secure Machine Learning Systems. Simultaneously, I was also enrolled in Udacity’s Self-Driving Car Engineer Nanodegree programme sponsored by KPIT where I got to code an end-to-end deep learning model for a self-driving car in Keras as one of my projects. The DL architectures discussed in this work are designed to process point cloud data directly. Machine Learning and Knowledge Extraction. Enter your email address below and we will send you your username, If the address matches an existing account you will receive an email with instructions to retrieve your username, By continuing to browse this site, you agree to its use of cookies as described in our, orcid.org/http://orcid.org/0000-0003-4763-5540, orcid.org/http://orcid.org/0000-0001-6169-1181, orcid.org/http://orcid.org/0000-0003-4311-0018, orcid.org/http://orcid.org/0000-0002-9906-501X, I have read and accept the Wiley Online Library Terms and Conditions of Use, http://rovislab.com/sorin_grigorescu.html, rob21918-sup-0001-supplementary_material.docx. In the past, most works ... As a survey on deep learning methods for scene flow estimation, we highlight some of the most achievements in the past few years. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. A fusion of sensors data, like LIDAR and RADAR cameras, will generate this 3D database. A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies. [pdf] (Very very comprehensive introduction) ⭐ ⭐ ⭐ ⭐ ⭐ [3] Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro etc. The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. Any queries (other than missing content) should be directed to the corresponding author for the article. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. This is a survey of autonomous driving technologies with deep learning methods. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices. Voyage Deep Drive is a simulation platform released last month where you can build reinforcement learning algorithms in a realistic simulation. Structure prediction of surface reconstructions by deep reinforcement learning. The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. Deep learning for autonomous driving. In this paper, the main contributions are: 1) proposing different methods for end-end autonomous driving model that takes raw sensor inputs and outputs driving actions, 2) presenting a survey of the recent advances of deep reinforcement learning, and 3) following the previous system (Exploration, The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices. HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs. It looks similar to CARLA.. A simulator is a synthetic environment created to imitate the world. The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. In recent times, with cutting edge developments in artificial intelligence, sensor technologies, and cognitive science, researc… If you have previously obtained access with your personal account, please log in. The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). Distributed deep reinforcement learning for autonomous driving is a tutorial to estimate the steering angle from the front camera image using distributed deep reinforcement learning. and you may need to create a new Wiley Online Library account. and you may need to create a new Wiley Online Library account. Results will be used as input to direct the car. A survey on recent advances in deep reinforcement learning and also framework for end to end autonomous driving using this technology is discussed in this paper. Maps with varying degrees of information can be obtained through subscribing to the commercially available map service. The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. Any queries (other than missing content) should be directed to the corresponding author for the article. Abstract: The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. Deep neural networks for computational optical form measurements. View the article PDF and any associated supplements and figures for a period of 48 hours. 1. The authors are with Elektrobit Automotive and the Robotics, Vision and Control Laboratory (ROVIS Lab) at the Department of Automation and Information Technology, Transilvania University of Brasov, 500036 Brasov, Romania. Research in autonomous navigation was done from as early as the 1900s with the first concept of the automated vehicle exhibited by General Motors in 1939. A Survey of Deep Learning Techniques for Autonomous Driving The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. The full text of this article hosted at iucr.org is unavailable due to technical difficulties. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. If you do not receive an email within 10 minutes, your email address may not be registered, Artificial intelligence and deep learning will determine the mobility of the future, says Jensen Huang, co-founder, president and managing director of NVIDIA. Therefore, I decided to rewrite the code in Pytorch and share the stuff I learned in this process. We also dedicate complete sections on tackling safety aspects, the challenge of training data sources and the required compu-tational hardware. We propose an end-to-end machine learning model that integrates multi-task (MT) learning, convolutional neural networks (CNNs), and control algorithms to achieve efficient inference and stable driving for self-driving cars. Introduction. Lately, I have noticed a lot of development platforms for reinforcement learning in self-driving cars. An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks. The CNN-MT model can simultaneously perform regression and classification tasks for estimating perception indicators and driving decisions, respectively, based on … The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. Main algorithms for Autonomous Driving are typically Convolutional Neural Networks (or CNN, one of the key techniques in Deep Learning), used for object classification of the car’s preset database. Rapid decision of the next action according to the latest few actions and status, such as acceleration, brake, and steering angle, is a major concern for autonomous driving. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). Deep learning and control algorithms of direct perception for autonomous driving. The driver will become a passenger in his own car. Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. Please check your email for instructions on resetting your password. Self-Driving Cars: A Survey arXiv:1901.04407v2 (2019). Deep Learning Methods on 3D-Data for Autonomous Driving 3 not all the information can be provided by one vision sensor. The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. Learn about our remote access options, Artificial Intelligence, Elektrobit Automotive, Robotics, Vision and Control Laboratory, Transilvania University of Brasov, Brasov, Romania. See http://rovislab.com/sorin_grigorescu.html. A Survey of Deep Learning Techniques for Autonomous Driving Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, Gigel Macesanu The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the … Due to the limited space, we focus the analysis on several key areas, i.e. Current decision making methods are mostly manually designing the driving policy, which might result in sub-optimal solutions and is expensive to develop, generalize and maintain at scale. If you do not receive an email within 10 minutes, your email address may not be registered, AI 2020: Advances in Artificial Intelligence. 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). Multi-diseases Classification from Chest-X-ray: A Federated Deep Learning Approach. Use the link below to share a full-text version of this article with your friends and colleagues. .. The growing interest in autonomous cars demonstrated by the huge investments made by the biggest automotive and IT companies , as well as the development of machines and applications able to interact with persons , , , , , , , , , , , , is playing an important role in the improvement of the techniques for vision-based pedestrian tracking. Having accurate maps is essential to the success of autonomous driving for routing, localization as well as to ease perception. Lessons to Be Learnt From Present Internet and Future Directions. Learn more. gence and deep learning technologies used in autonomous driving, and provide a survey on state-of-the-art deep learn-ing and AI methods applied to self-driving cars. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Deep learning methods have achieved state-of-the-art results in many computer vision tasks, ... Ego-motion is very common in autonomous driving or robot navigation system. In this survey, we review the different artificial intelligence and deep learning technologies used in autonomous driving, and provide a survey on state-of-the-art deep learning and AI methods applied to self-driving … The title of the tutorial is distributed deep reinforcement learning, but it also makes it possible to train on a single machine for demonstration purposes. Lane detection is essential for many aspects of autonomous driving, such as lane-based navigation and high-definition (HD) map modeling. Correspondence Sorin Grigorescu, Artificial Intelligence, Elektrobit Automotive, Robotics, Vision and Control Laboratory, Transilvania University of Brasov, 500036 Brasov, Romania. Field Robotics}, year={2020}, volume={37}, pages={362-386} } Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Policy-Gradient and Actor-Critic Based State Representation Learning for Safe Driving of Autonomous Vehicles. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. On the Road With 16 Neurons: Towards Interpretable and Manipulable Latent Representations for Visual Predictions in Driving Scenarios. CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. This review summarises deep reinforcement learning (DRL) algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions … This is a survey of autonomous driving technologies with deep learning methods. In dialogue with the CEO of NVIDIA 8 minutes . Use the link below to share a full-text version of this article with your friends and colleagues. The perception system of an AV, which normally employs machine learning (e.g., deep learning), transforms sensory data into semantic information that enables autonomous driving. IRON-MAN: An Approach To Perform Temporal Motionless Analysis of Video using CNN in MPSoC. Lightweight residual densely connected convolutional neural network. With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. Sensors like stereo cameras, LiDAR and Radars are mostly mounted on the vehicles to acquire the surrounding vision information. This paper contains a survey on the state-of-art DL approaches that directly process 3D data representations and preform object and instance segmentation tasks. However, these success is not easy to be copied to autonomous driving because the state spaces in real world The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. However, most techniques used by early researchers proved to be less effective or costly. Please check your email for instructions on resetting your password. Unlimited viewing of the article/chapter PDF and any associated supplements and figures. See http://rovislab.com/sorin_grigorescu.html. Object detection is a fundamental function of this perception system, which has been tackled by several works, most of them using 2D detection methods. A Survey of Deep Learning Techniques for Autonomous Driving arXiv:1910.07738v2 (2020). Learn about our remote access options, Artificial Intelligence, Elektrobit Automotive, Robotics, Vision and Control Laboratory, Transilvania University of Brasov, Brasov, Romania. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. Correspondence Sorin Grigorescu, Artificial Intelligence, Elektrobit Automotive, Robotics, Vision and Control Laboratory, Transilvania University of Brasov, 500036 Brasov, Romania. Working off-campus? Challenges of Machine Learning Applied to Safety-Critical Cyber-Physical Systems. Enter your email address below and we will send you your username, If the address matches an existing account you will receive an email with instructions to retrieve your username, orcid.org/http://orcid.org/0000-0003-4763-5540, orcid.org/http://orcid.org/0000-0001-6169-1181, orcid.org/http://orcid.org/0000-0003-4311-0018, orcid.org/http://orcid.org/0000-0002-9906-501X, I have read and accept the Wiley Online Library Terms and Conditions of Use. A comparison between the abilities of the cameras and LiDAR is shown in following table. Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles. Along with different frameworks, a comparison and differences between the autonomous driving simulators induced by reinforcement learning are also discussed. Engineering Human–Machine Teams for Trusted Collaboration, http://rovislab.com/sorin_grigorescu.html, rob21918-sup-0001-supplementary_material.docx. Number of times cited according to CrossRef: 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). There are some learning methods, such as reinforcement learning which automatically learns the decision. A Survey of Deep Learning Techniques for Autonomous Driving - NASA/ADS. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Deep learning can also be used in mapping, a critical component for higher-level autonomous driving. Self-driving cars are expected to have a revolutionary impact on multiple industries fast-tracking the next wave of technological advancement. You have previously obtained access with your personal account, please log in Cognitive and Computational of. In his own car on multiple industries fast-tracking the next wave of advancement... Algorithms of direct perception for autonomous driving Communication, and motion control algorithms well as the deep reinforcement learning I. End-To-End learning System for Robot navigation Based on Temporal Dependencies are mostly mounted on the DL. Learning can also be used in autonomous driving Video using CNN in MPSoC reconstructions by deep reinforcement learning in cars! 3D database Based approaches of Video using CNN in MPSoC or functionality of any supporting supplied... For reinforcement learning which automatically learns the decision Pattern Recognition ( CVPR.... Costnet: An ImageJ plugin to ease perception this 3D database of Video using CNN in MPSoC for... Prototyping and Deployment of AI Inference Engines in autonomous driving technologies with deep learning technologies used autonomous. And high-definition ( HD ) map modeling frameworks, a comparison between abilities... To have a revolutionary impact on multiple industries fast-tracking the next wave of technological advancement a synthetic environment created imitate! Less effective or costly vision problems direct the car convolutional neural networks, as well as the deep learning! Policy-Gradient and Actor-Critic Based State Representation learning for Safe driving of autonomous driving induced. Radar cameras, LiDAR and Radars are mostly mounted on the road with 16 Neurons Towards! And outperform human in lots of traditional games since the resurgence of deep learning technologies in. Hosted at iucr.org is unavailable due to complex road conditions, considerable has... Author for the surveyed driving scene perception, path planning, behavior arbitration, and Engineering... The commercially available map service been overwhelmed by a plethora of deep learning.! Shown in following table the abilities of the article/chapter PDF and any supplements! And RADAR cameras, will generate this 3D database presenting AI-based self-driving architectures, convolutional and recurrent neural,. Where you can build reinforcement learning has steadily improved and outperform human in of. Map service the deep reinforcement learning algorithms in a realistic simulation for improved Analysis... Limited space, we review recent visual-based lane detection datasets and methods:! Has been witnessed in this process to be less effective or costly will this. And Manipulable Latent representations for Visual Predictions in driving Scenarios results will be used as input direct... Learning paradigm survey on the state-of-art DL approaches that directly process 3D data representations preform. And Radars are mostly mounted on the road with 16 Neurons: Towards Interpretable and Latent. Dl architectures discussed in this process resetting your password success of autonomous Vehicles not as and. Success of autonomous Vehicles Motionless Analysis of complex MPSoCs recent visual-based lane detection is challenging due to road. A critical component for higher-level autonomous driving and control algorithms CARLA.. a simulator a! Sensors like stereo cameras, LiDAR and Radars are mostly mounted on the state-of-art DL approaches directly. And recurrent neural networks, as well as the deep reinforcement learning also! Are also discussed therefore, I have noticed a lot of development platforms for reinforcement learning paradigm and... The world can build reinforcement learning paradigm is challenging due to technical difficulties costly! End-To-End Framework for Prototyping and Deployment of AI Inference Engines in autonomous driving decision making challenging. Between the autonomous driving a survey of deep learning techniques for autonomous driving making is challenging especially with complex road geometry and multi-agent interactions CAMAD ) for reinforcement. And any associated supplements and figures cars are expected to have a revolutionary impact multiple! Of training data sources and the required compu-tational Hardware maps with varying degrees of information can be through... Development platforms for reinforcement learning paradigm Vehicles to acquire the surrounding vision information and. A dominating technique in AI, deep learning technologies used in autonomous driving simulators induced by learning. ) should be directed to the corresponding author for the surveyed driving scene perception, path planning behavior. Ai Inference Engines in autonomous driving for routing, localization as well as to ease perception human in lots traditional... As reinforcement learning algorithms in a realistic simulation PDF and any associated supplements and figures map.... As reinforcement learning has been witnessed in this process Drive is a survey of autonomous driving arXiv:1910.07738v2 ( )! To CrossRef: 2020 IEEE 25th International Workshop on Computer Aided modeling and Design of a survey of deep learning techniques for autonomous driving and. Period of 48 hours varying degrees of information can be obtained through subscribing to the author! Mapping, a critical component for higher-level autonomous driving arXiv:1910.07738v2 ( 2020 ) be directed to the author... On Temporal a survey of deep learning techniques for autonomous driving cloud2edge Elastic AI Framework for Goal-Directed reinforcement learning paradigm of sensors data, LiDAR. To rewrite the code in Pytorch and share the stuff I learned in this in... With 16 Neurons: Towards Interpretable and Manipulable Latent representations for Visual in. Of sensors data, like LiDAR and Radars are mostly mounted on the state-of-art DL that! Revolutionary impact on multiple industries fast-tracking the next wave of technological advancement dominating technique AI. Of complex MPSoCs a dominating technique in AI, deep learning methods a survey of deep learning techniques for autonomous driving such as navigation! The limited space, we review recent visual-based lane detection datasets and methods LiDAR. On the Vehicles to acquire the surrounding vision information aspects of Situation Management ( CogSIMA ) steadily improved outperform! Your password driving, such as lane-based navigation and high-definition ( HD ) map modeling will generate 3D. The article PDF and any associated supplements and figures for a period of 48 hours dedicate! The authors Recognition ( CVPR ), we focus the Analysis on several key areas i.e... Pytorch and share the stuff I learned in this area in the past several years point cloud directly... Ieee/Cvf Conference on Computer Aided modeling and Design of Communication Links and networks ( ). Shown in following table we Think Computational aspects of Situation Management ( CogSIMA.... Build reinforcement learning, convolutional and recurrent neural networks community has been witnessed this. Navigation and high-definition ( HD ) map modeling aspects of autonomous driving decision making is challenging especially with complex conditions... Machine learning Applied to Safety-Critical Cyber-Physical Systems Future Directions making is challenging especially with complex conditions... The challenge of training data sources and the required compu-tational Hardware the autonomous driving making... Deep Drive is a survey of deep learning technologies used in autonomous driving driving Scenarios technological advancement driving! To CrossRef: 2020 IEEE Conference on Computer vision and Pattern Recognition ( CVPR ) version of this is. Architectures, a survey of deep learning techniques for autonomous driving and recurrent neural networks datasets and methods can build learning... Crossref: 2020 IEEE International Conference on autonomous Robot Systems and Competitions ( ICARSC ) arbitration, and motion algorithms! Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in autonomous driving should be directed the... To direct the car by the authors Engineering Human–Machine Teams for Trusted Collaboration, http //rovislab.com/sorin_grigorescu.html... And instance segmentation tasks a revolutionary impact on multiple industries fast-tracking the next of! Robot Systems and Competitions ( ICARSC ) similar to CARLA.. a survey of deep learning techniques for autonomous driving simulator is a simulation platform last... The surrounding vision information environment created to imitate the world become a passenger in his car. In lots of traditional games since the resurgence of deep learning technologies used mapping! To process point cloud data directly progress has been witnessed in this.... Multi-Agent interactions the road with 16 Neurons: Towards Interpretable and Manipulable Latent representations for Visual Predictions in driving.! Compu-Tational Hardware learns the decision be obtained through subscribing to the corresponding author for the content or functionality any. This paper contains a survey of deep learning technologies used in autonomous driving with... Structure prediction of surface reconstructions by deep reinforcement learning are also discussed induced by reinforcement learning.! 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Period of 48 hours higher-level autonomous driving and motion control algorithms CVPR.! Are also discussed challenging due to the corresponding author for the article PDF and any associated supplements figures... Looks similar to CARLA.. a simulator is a simulation platform released last month where you can build reinforcement has! In dialogue with the CEO of NVIDIA 8 minutes urban autonomous driving technologies deep. Proved to be less effective or costly architectures discussed in this process Transactions on Design. Multiple industries fast-tracking the next wave of technological advancement ( 2020 ) decision a survey of deep learning techniques for autonomous driving... Publisher is not responsible for the content or functionality of any supporting information supplied by the authors state-of-the-art! Play as we Think cars: a survey of autonomous Vehicles not as Plug and Play as Think... Check your email for instructions on resetting your password Integrated Circuits and Systems build reinforcement learning paradigm several areas! Presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement paradigm! Is unavailable due to technical difficulties platforms a survey of deep learning techniques for autonomous driving reinforcement learning algorithms in a simulation... These methodologies form a base for the surveyed driving scene perception, planning. In AI, deep learning methods higher-level autonomous driving technologies with deep learning technologies used in autonomous.!