•  Getting data is the main effort in Machine Learning. Yehya Abouelnaga 3. Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADASFlora Dellinger, Thomas Boulay, Diego Mendoza Barrenechea, Said El-Hachimi, Isabelle Leang, Fabian Bürgerpaper | video | poster 38 Zhuwen Li deep-learning-coursera / Structuring Machine Learning Projects / Week 2 Quiz - Autonomous driving (case study).md Go to file Go to file T; Go to line L; Copy path Kulbear Create Week 2 Quiz - Autonomous driving (case study).md.   •  A car must ‘learn’ and adapt to the unpredictable behavior of other cars nearby. Peyman Yadmellat Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised ModelsNick Lamm, Shashank Jaiprakash, Malavika Srikanth, Iddo Droripaper | video | poster 11 Machine Learning Developer – Autonomous Driving A Tier 1 Embedded Software company based in Munich are looking for multiple Machine Learning Engineers to join their expanding company. Oliver Bringmann YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-DesignYuxuan Cai*, Geng Yuan*, Hongjia Li*, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, Yanzhi Wangpaper | video | poster 20   •  is the Chief Scientist for Intelligent Systems at Intel. Register for NeurIPS Silviu Homoceanu Chat with authors during the GatherTown poster sessions (9:20am, 12:00pm, 2:20pm PST), Assistant Professor, University of Toronto, Research Associate, University of California Berkeley, Associate Professor, University of Washington, The CARLA Autonomous Driving Challenge 2020 winners will present their solutions as part of the workshop. Autonomous development has shown that machine learning can be successfully and reliably used for virtually all mobility functions when it’s been implemented. Conditional Imitation Learning Driving Considering Camera and LiDAR FusionHesham Eraqi, Mohamed Moustafa, Jens Honerpaper | video | poster 13 A formal modeling language is presented to model the stochastic behaviors in the uncertain environment.   •  When you skip a song, it can change satellite radio stations for you when the disliked song is about to be played.   •  Ben Caine Machine Learning and Autonomous Driving It is not an exaggeration to state that every single vehicle capable of autonomous driving is an embodiment of machine learning technology.   •    •    •  Hesham Eraqi Tanmay Agarwal Extracting Traffic Smoothing Controllers Directly From Driving Data using Offline RLThibaud Ardoin, Eugene Vinitsky, Alexandre Bayenpaper | video | poster 41   •  EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational ReasoningJiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choipaper | video | poster 8 Patrick Nguyen   •  Modeling Affect-based Intrinsic Rewards for Exploration and LearningDean Zadok, Daniel McDuff, Ashish Kapoorpaper | video | poster 64. is a postdoctoral researcher at UC Berkeley working on probabilistic models and planning for autonomous vehicles.   •  Machine Learning Algorithms in Autonomous Driving Autonomous cars are very closely associated with Industrial IoT. Physically Feasible Vehicle Trajectory PredictionHarshayu Girase*, Jerrick Hoang*, Sai Yalamanchi, Micol Marchetti-Bowickpaper | video | poster 55   •  Xiao-Yang Liu   •  is a postdoctoral researcher at UC Berkeley, focusing on understanding, forecasting, and control with computer vision and machine learning. Evgenia Rusak Currently, machine learning is in an intermediate stage were it has begun to become mainstream thinking but has not yet become commonplace. As an application of ML, autonomous driving has the potential to greatly improve society by reducing road accidents, giving independence to those unable to drive, and even inspiring younger generations with tangible examples of ML-based technology clearly visible on local streets. At Waymo, machine learning plays a key role in nearly every part of our self-driving system.   •  Further information regarding technologies used, providers, storage duration, recipients, transfer to third countries, and changing your settings, including essential (i.e. While machine learning and artificial intelligence (AI) possess tremendous potential in applications such as autonomous driving and Industry 4.0, they also bring new challenges with respect to safety and dependability. Anki's Cozmo robot has a built in camera and an extensive python SDK, everything we need for autonomous driving. We thank those who help make this workshop possible! By selecting "accept and continue" you consent to the use of the aforementioned technologies and to the transfer of information to third parties. Certified Interpretability Robustness for Class Activation MappingAlex Gu, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Danielpaper | video | poster 10 Further, the interaction between ML subfields towards a common goal of autonomous driving can catalyze interesting inter-field discussions that spark new avenues of research, which this workshop aims to promote. The trend is no more evident than in the self-driving or autonomous vehicle space where advances in ML and AI are not just for the major auto manufacturers, however. A fusion of sensors data, like LIDAR and RADAR cameras, will generate this 3D database. 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Mennatullah Siam In order for autonomous vehicles (AVs) to safely navigate streets, whether empty or in rush-hour traffic, requires the ability to make decisions. Autonomous or self-driving cars are beginning to occupy the same roads the general public drives on. Ahmad El Sallab Bézier Curve Based End-to-End Trajectory Synthesis for Agile Autonomous DrivingTrent Weiss, Varundev Suresh Babu, Madhur Behlpaper | video | poster 39   •  Deep Reinforcement Learning framework for Autonomous Driving Ahmad El Sallab, Mohammed Abdou, Etienne Perot, Senthil Yogamani Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Real-time Semantic and Class-agnostic Instance Segmentation in Autonomous DrivingEslam Mohamed*, Mahmoud Ewaisha*, Mennatullah Siam, Hazem Rashed, Senthil Yogamani, Waleed Hamdy, Muhammad Helmi, Ahmad ElSallabpaper | video | poster 7 It analyzes possible outcomes and makes a decision based on the best one, then learns from it. IoT combined with other technologies such as machine learning, artificial intelligence, local computing etc are providing the essential technologies for autonomous cars. Ashutosh Singh   •  This article aims to explain why data management is such critical for Machine Learning – especially for ML-powered autonomous driving. Histogram of oriented gradients (HOG) is one of the most basic machine learning algorithms for autonomous driving and computer vision. Thomas Adler Fabian Hüger A user’s in-cabin experience can be enhanced with machine learning. Hitesh Arora   •  Reinforcement Learning Based Approach for Multi-Vehicle Platooning Problem with Nonlinear Dynamic BehaviorAmr Farag, Omar Abdelaziz, Ahmed Hussein, Omar Shehatapaper | video | poster 32 Machine Learning for Autonomous Control of a Cozmo Robot.   •  Undoubtedly, parallel parking and tight perpendicular parking are a source of frustration for many drivers.   •  That can make many people nervous about a vehicle’s ability to make safe decisions. technically or functionally essential) cookies, can be found in the privacy policy and cookie information table. Hua Wei here, Single Shot Multitask Pedestrian Detection and Behavior PredictionPrateek Agrawal, Pratik Prabhanjan Brahmapaper | video | poster 57 Leading the Self-driving Car Innovation in Asia, Learning Decision-making Behaviors from Demonstrations based on Adversarial Inverse Reinforcement Learning, On Human-Robot Interaction and Crossing a Street in the Era of Autonomous Vehicles, Online Learning for Adaptive Robotic Systems, Learning a Multi-Agent Simulator from Offline Demonstrations, Building HDmap using Mass Production Data, Machine Learning for Safety-Critical Robotics Applications. Ameya Joshi Ravi Kiran Matthias Fahrland Privacy   •    •  Enabling Virtual Validation: from a single interface to the overall chain of effects And while a human driver might be able to perform one evasive maneuver, AVs could potentially perform complex actions where a human could not avoid a collision.   •  For AVs, algorithms take the place of a human brain in determining the correct action to perform. 3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local RepresentationNetalee Efrat, Max Bluvstein, Shaul Oron, Dan Levi, Noa Garnett, Bat El Shlomopaper | video | poster 24 MODETR: Moving Object Detection with TransformersEslam Bakr, Ahmad ElSallab, Hazem Rashedpaper | video | poster 30   •    •  The key goal of active learning is to determine which data needs to be manually labeled.   •  Wei-Lun Chao A Distributed Delivery-Fleet Management Framework using Deep Reinforcement Learning and Dynamic Multi-Hop RoutingKaushik Manchella, Marina Haliem, Vaneet Aggarwal, Bharat Bhargavapaper | video | poster 53 It can realistically trim minutes off a commute time.   •  Mark Schutera Additionally, all participants are invited to submit a technical report (up to 4 pages) describing their submissions. Until today, there are few Machine Learning projects without the “surprise” at some point that data is missing, corrupted, expensive, hard to obtain, or just arriving far later than expected. Previous workshops in 2016, 2017, 2018 and 2019 enjoyed wide participation from both academia and industry. You can revoke this consent at any time with effect for the future here. Renhao Wang Energy-Based Continuous Inverse Optimal ControlYifei Xu, Jianwen Xie, Tianyang Zhao, Chris Baker, Yibiao Zhao, Ying Nian Wupaper | video | poster 2 It can also leave a parking space and return to the driver’s position driverless, allowing parking spots with tighter tolerances to be used. Amitangshu Mukherjee   •  This can help keep pedestrians safer plus avoid distracted driving accidents more often.   •  Jaekwang Cha SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature ExtractionJaehoon Choi*, Dongki Jung*, Donghwan Lee, Changick Kimpaper | video | poster 31 A unified framework is proposed for uncertainty modeling and runtime verification of autonomous vehicles driving control. These sensors generate a massive amount of data. Bringing together machine learning and sensor fusion using data-driven measurement models; Application Level Monitor Architecture for Level 4 Automated Driving; FOCUS II: Validation of data fusion systems.   •  is a PhD student at the University of Oxford working on explainability in autonomous vehicles. Haar Wavelet based Block Autoregressive Flows for TrajectoriesApratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz, Bernt Schielepaper | video | poster 21 Uncertainty-aware Vehicle Orientation Estimation for Joint Detection-Prediction ModelsHenggang Cui, Fang-Chieh Chou, Jake Charland, Carlos Vallespi-Gonzalez, Nemanja Djuricpaper | video | poster 18 Machine learning algorithms are now used extensively to find solutions to different challenges ranging from financial market predictions to self-driving cars. Supervised learning algorithms like the support vector machine, linear regression, and deep learning are used to form the predictive models. Tanvir Parhar Sanjeev is also a recipient of the Leading 4 0 Under 40 Data Scientists in India award, at the Machine Learning Developers Summit for his research in autonomous driving technology over the past four years, which enabled autonomous driving on Indian roads — world’s toughest test ground for autonomous driving. 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