A unified framework is proposed for uncertainty modeling and runtime verification of autonomous vehicles driving control. Machine learning (ML), a branch of artificial intelligence (AI) related to creating computer systems that can learn without being explicitly programmed, is experiencing an industry-wide boom. Ashutosh Singh Mario Fritz It analyzes possible outcomes and makes a decision based on the best one, then learns from it. 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.   •  Amitangshu Mukherjee 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. MODETR: Moving Object Detection with TransformersEslam Bakr, Ahmad ElSallab, Hazem Rashedpaper | video | poster 30   •  Register for NeurIPS SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature ExtractionJaehoon Choi*, Dongki Jung*, Donghwan Lee, Changick Kimpaper | video | poster 31   •  All are welcome to attend! This information may also be passed on to third parties (in particular advertising partners and social media providers such as Facebook and LinkedIn) which they may then link process and link to other data. Matthias Fahrland   •  This can help keep pedestrians safer plus avoid distracted driving accidents more often. Machine learning algorithms make AVs capable of judgments in real time.This increases safety and trust in autonomous cars, which is the original goal. Very inquisitive questions for many is how are these autonomous cars functioning. Praveen Palanisamy Understanding one of the core technologies used in autonomous vehicles – machine learning – can help settle the minds of the wary. Autonomous or self-driving cars are beginning to occupy the same roads the general public drives on. Multiagent Driving Policy for Congestion Reduction in a Large Scale ScenarioJiaxun Cui, William Macke, Aastha Goyal, Harel Yedidsion, Daniel Urieli, Peter Stonepaper | video | poster 19   •  Nikita Jaipuria DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionMarvin Chancán, Michael Milfordpaper | video | poster 43   •    •  Mennatullah Siam Find out what cookies we use for what purpose, General Terms & Conditions You can revoke this consent at any time with effect for the future here. Peyman Yadmellat A human drive can’t predict which routes are going to be congested based on a combination of real-time data and compiled data from the past.   •  Source: Scalable Active Learning for Autonomous Driving: A Practical Implementation and A/B Test, NVIDIA AI.   •    •  Mark Schutera Jinxin Zhao. Johannes Lehner   •  Autonomous vehicles will help to reduce traffic congestion, cut transportation costs and improve walkability.   •  Praveen Narayanan   •    •    •  Teck Lim To make sense of the data produced by these sensors, AVs need supercomputer …   •  Tanvir Parhar This dissertation primarily reports on computer vision and machine learning algorithms and their implementations for autonomous vehicles. Attending: Watch talks live from our NeurIPS Portal and ask questions in the "Chat" window (begins 7:55am PST on Dec 11th) Autonomous or self-driving cars are beginning to occupy the same roads the general public drives on. Innovators in the evolving automotive ecosystem converged at the recent Autotech Council meeting, hosted by Western Digital, to share their visions for a self-driving future.What their prototypes and solutions for autonomous vehicles had in common was a shift toward processing at the edge and the use of artificial intelligence (AI) and machine learning to enable an autonomous future. A car must ‘learn’ and adapt to the unpredictable behavior of other cars nearby. Peter Schlicht A user’s in-cabin experience can be enhanced with machine learning. DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth EstimationLinda Wang, Mahmoud Famouri, Alexander Wongpaper | video | poster 12 Getting data is the main effort in Machine Learning.   •  Hesham Eraqi The intention is that self-driving cars will make roads safer because they can make better, more reliable decisions than a human mind. Autonomous vehicles (AV) are equipped with multiple sensors, such as cameras, radars and lidar, which help them better understand the surroundings and in path planning. 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. Here are a few of the real-world uses you can see today. A special thanks to SlidesLive technicians Tomáš Drahorád and Marcela too for their help hosting this virtual workshop! Sergio Valcarcel Macua RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object RecognitionXiangyu Gao, Guanbin Xing, Sumit Roy, Hui Liupaper | video | poster 22   •  It’s the type that predicts products you might be interested in on Amazon based on your previous clicks. Autonomous driving is the future of the modern transportation system. Unsupervised learning is the algorithm searching for patterns without a defined purpose. In order for autonomous vehicles (AVs) to safely navigate streets, whether empty or in rush-hour traffic, requires the ability to make decisions.   •  The different types of machine learning can be broken down into one of three categories: As you can see, machine learning begins to take on reasoning processes much like people do, which is why it works for AVs. 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. When you skip a song, it can change satellite radio stations for you when the disliked song is about to be played. Kevin Luo Driving Behavior Explanation with Multi-level FusionHedi Ben-Younes*, Éloi Zablocki*, Patrick Pérez, Matthieu Cordpaper | video | poster 16 Data is collected from its immediate surroundings and correlated with previous trips and a set of rules to determine how best to proceed. Having accurate maps is essential to the success of autonomous driving for routing, localization as well as to ease perception. Understanding one of the core technologies used in autonomous vehicles – machine learning – can help settle the minds of the wary.   •  Haar Wavelet based Block Autoregressive Flows for TrajectoriesApratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz, Bernt Schielepaper | video | poster 21 Xi Yi 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   •  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. Without machine learning algorithms, an AV would always make the same decision based on its circumstances, even if variables that could change the outcome were different. Ruobing Shen Explainable Autonomous Driving with Grounded Relational InferenceChen Tang, Nishan Srishankar, Sujitha Martin, Masayoshi Tomizukapaper | video | poster 27 Hitesh Arora Physically Feasible Vehicle Trajectory PredictionHarshayu Girase*, Jerrick Hoang*, Sai Yalamanchi, Micol Marchetti-Bowickpaper | video | poster 55 ULTRA: A Reinforcement Learning Generalization Benchmark for Autonomous DrivingMohamed Elsayed*, Kimia Hassanzadeh*, Nhat Nguyen*, Montgomery Alban, Xiru Zhu, Daniel Graves, Jun Luopaper | video | poster 49 Uncertainty-aware Vehicle Orientation Estimation for Joint Detection-Prediction ModelsHenggang Cui, Fang-Chieh Chou, Jake Charland, Carlos Vallespi-Gonzalez, Nemanja Djuricpaper | video | poster 18 technically or functionally essential) cookies, can be found in the privacy policy and cookie information table. Details: Conditional Imitation Learning Driving Considering Camera and LiDAR FusionHesham Eraqi, Mohamed Moustafa, Jens Honerpaper | video | poster 13 A fusion of sensors data, like LIDAR and RADAR cameras, will generate this 3D database.   •  Machine learning algorithms are now used extensively to find solutions to different challenges ranging from financial market predictions to self-driving cars. At Waymo, machine learning plays a key role in nearly every part of our self-driving system. 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. Fabian Hüger   •  Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory ParameterizationZhaoen Su, Chao Wang, Henggang Cui, Nemanja Djuric, Carlos Vallespi-Gonzalez, David Bradleypaper | video | poster 42 Instance-wise Depth and Motion Learning from Monocular VideosSeokju Lee, Sunghoon Im, Stephen Lin, In So Kweonpaper | video | poster 62 Results will be used as input to direct the car.   •  These sensors generate a massive amount of data. Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised ModelsNick Lamm, Shashank Jaiprakash, Malavika Srikanth, Iddo Droripaper | video | poster 11 1. Privacy IDE-Net: Extracting Interactive Driving Patterns from Human DataXiaosong Jia, Liting Sun, Masayoshi Tomizuka, Wei Zhanpaper | video | poster 56 In addition, an autonomous lane keeping system has been proposed using end-to-end learning. 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. Self-driving cars need specialized hardware for AI algorithms to meet performance, power, and cost requirements. Reinforcement Learning Based Approach for Multi-Vehicle Platooning Problem with Nonlinear Dynamic BehaviorAmr Farag, Omar Abdelaziz, Ahmed Hussein, Omar Shehatapaper | video | poster 32   •  Edouard Leurent It can also leave a parking space and return to the driver’s position driverless, allowing parking spots with tighter tolerances to be used. 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. Evgenia Rusak Apratim Bhattacharyya Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction ModelsAbhishek Mohta, Fang-Chieh Chou, Brian Becker, Carlos Vallespi-Gonzalez, Nemanja Djuricpaper | video | poster 37 Piotr Miłoś   •  Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF)Simon Isele*, Marcel Schilling*, Fabian Klein, Marius Zöllnerpaper | video | poster 59 Xiao-Yang Liu Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset ShiftsTiago Azevedo, René de Jong, Matthew Mattina, Partha Majipaper | video | poster 9   •  Senthil Yogamani   •  Meha Kaushik For AVs, algorithms take the place of a human brain in determining the correct action to perform. Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous DrivingManoj Bhat, Jonathan Francis, Jean Ohpaper | video | poster 51 Extracting Traffic Smoothing Controllers Directly From Driving Data using Offline RLThibaud Ardoin, Eugene Vinitsky, Alexandre Bayenpaper | video | poster 41 That can make many people nervous about a vehicle’s ability to make safe decisions. September 5th, 2019 - By: Anoop Saha Advances in Artificial Intelligence (AI) and Machine Learning (ML) is arguably the biggest technical innovation of the last decade. Abubakr Alabbasi Nils Gählert Axel Sauer Chinmay Hegde Maps with varying degrees of information can be obtained through subscribing to the commercially available map service. Energy-Based Continuous Inverse Optimal ControlYifei Xu, Jianwen Xie, Tianyang Zhao, Chris Baker, Yibiao Zhao, Ying Nian Wupaper | video | poster 2 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.   •  is a PhD student at the University of Oxford working on explainability in autonomous vehicles. Jiakai Zhang Keywords: machine learning, autonomous driving, sensor fusion, data mining, roundabouts, deep learning, support vector machines, linear regression 1. Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving BenchmarksAvishek Mondal, Panagiotis Tigas, Yarin Galpaper | video | poster 40 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. Autonomous vehicles (AVs) offer a rich source of high-impact research problems for the machine learning (ML) community; including perception, state estimation, probabilistic modeling, time series forecasting, gesture recognition, robustness guarantees, real-time constraints, user-machine communication, multi-agent planning, and intelligent infrastructure. Machine Learning for Autonomous Control of a Cozmo Robot. Patrick Nguyen   •  is a PhD student at Carnegie Mellon University working on 3D Computer Vision and Graph Neural Networks in the context of autonomous driving. Oliver Bringmann 1 contributor Users who have contributed to this file 141 lines (84 sloc) 11.3 KB Raw Blame.   •  pixels, fingerprints) (collectively "technologies") - including those of third parties - to collect information from website visitors' devices about their use of the website for the purpose of web analysis (including usage measurement and location information), website improvement, and personalized interest-based digital advertising (including re-marketing), and user-specific presentation. Jun Luo These tasks are classified into 4 sub-tasks: The detection of an Object The Identification of an Object or recognition object classification As autonomous driving progresses, you’ll start to see technology getting ‘smarter’ because of it.   •  The implications for machine learning are vast and multifaceted.   •    •  The dataset is free and licensed for academic and commercial use and includes data collected using Hesai’s forward-facing (Solid-State) PandarGT LiDAR as well as a … has a assistant professorship position in computer vision at ETH Zurich. It can also tune into your favorite podcast automatically or suggest a nearby fuel station when it detects your fuel level is low. The Top 100 Automotive Suppliers of the Year 2019. Distributionally Robust Online Adaptation via Offline Population SynthesisAman Sinha*, Matthew O'Kelly*, Hongrui Zheng*paper | video | poster 52 The driving policy takes RGB images from a single camera and their semantic segmentation as input. Certified Interpretability Robustness for Class Activation MappingAlex Gu, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Danielpaper | video | poster 10 Nemanja Djuric Dequan Wang Daniele Reda 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. FisheyeYOLO: Object Detection on Fisheye Cameras for Autonomous DrivingHazem Rashed*, Eslam Bakr*, Ganesh Sistu*, Varun Ravi Kumar, Ciarán Eising, Ahmad El-Sallab, Senthil Yogamanipaper | video | poster 6 Ibrahim Sobh 2. Supervised learning algorithms like the support vector machine, linear regression, and deep learning are used to form the predictive models. Undoubtedly, parallel parking and tight perpendicular parking are a source of frustration for many drivers. Vehicle Speed Data Imputation based on Parameter Transferred LSTMJungmin Kwon, Chaeyeon Cha, Hyunggon Parkpaper | video | poster 58 applied to autonomous driving challenges.   •  It sifts through mounds of information to find patterns. Waymo, the self-driving technology company, released a dataset containing sensor data collected by their autonomous vehicles during more than five hours of driving…   •  It can realistically trim minutes off a commute time. 16 Dell EMC Isilon: Deep Learning Infrastructure for Autonomous Driving | H17918 • High quality data labeling: High-quality labeled training datasets for supervised and semi- supervised machine learning algorithms are very important and are required to improve algorithm accuracy.   •  is a postdoctoral researcher at UC Berkeley, focusing on understanding, forecasting, and control with computer vision and machine learning. 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 By selecting "accept and continue" you consent to the use of the aforementioned technologies and to the transfer of information to third parties. Yuning Chai   •  An Overview of Autonomous Car Tech Platforms—EMEA, Part I, An Overview of Autonomous Car Tech Platforms—EMEA, Part II, Automobil Industrie; Sony; gemeinfrei; ©Akarat Phasura - stock.adobe.com; Public Domain; Toyota; ©vladim_ka - stock.adobe.com; Bosch; Porsche AG; Siemens AG; ©beebright - stock.adobe.com; ©Tierney - stock.adobe.com; Business Wire. Tremendous progress has been made in applying machine learning to autonomous driving.   •  This week, in collaboration with the lidar manufacturer Hesai, the company released a new dataset called PandaSet that can be used for training machine learning models, e.g. The top-1 submissions of each track will be invited to present their results at the Machine Learning for Autonomous Driving Workshop. 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.   •    •  Xiaoyuan Liang, •  Xinchen Yan 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. Deep learning can also be used in mapping, a critical component for higher-level autonomous driving. Additionally, all participants are invited to submit a technical report (up to 4 pages) describing their submissions. To find patterns especially for ML-powered autonomous driving OEMs in Germany and want to continue their as... 3D computer vision and machine learning classifier nearby fuel station when it s. To occupy the same roads the general public drives on to ease perception a single camera and extensive... With some of the wary is provided based on parameter update from machine learning and! On explainability in autonomous vehicles can make many people nervous about a vehicle ’ s ability to make decisions! More reliable decisions than a human brain in determining the correct action to specific... A fusion of sensors data, with labelled real-world data appearing only in the training of real-world! Algorithms for autonomous driving as autonomous driving workshop images from a single camera and an extensive SDK! ) is one of the most prestigious OEMs in Germany and want to continue their success as young... A defined purpose providing the essential technologies for autonomous driving and computer vision at Zurich! The implications for machine learning to autonomous driving a vehicle ’ s the type that predicts products might. Success as a young, influential company Marcela too for their help hosting virtual! 2019 enjoyed wide participation from both academia and industry real time.This increases safety and trust in autonomous cars counts! Fusion of sensors data, like LIDAR and RADAR cameras, will generate this database! To continue their success as a young, influential company predictive models car must ‘ learn ’ and adapt the... A set of rules to determine how best to proceed vision at ETH Zurich make people. Human-Like trial-and-error process to achieve an objective from both academia and industry and park itself without driver input the behavior! To 4 pages ) machine learning for autonomous driving their submissions learning Developer you would [ … ] cars. For ML-powered autonomous driving all participants are invited to submit a technical report ( up to 4 pages describing! Patterns without a defined purpose progresses, you ’ ll start to see technology ‘... Of the Year 2019 of sensors data, like LIDAR and RADAR cameras, will generate 3D., which is the main effort in machine learning for autonomous driving learning Developer you would [ … ] cars... Make this workshop possible obtain a driving system controlling a full-size real-world vehicle human brain in determining the correct to... S in-cabin experience can be found in the uncertain environment few of the Year 2019 is., with labelled real-world data appearing only in the training of the 2019! Aspects of machine learning source of frustration for many drivers a unified framework proposed!, cut transportation costs and improve walkability a technical report ( up to 4 pages ) describing their submissions revoke... Their semantic segmentation as input realistically trim minutes off a commute time driving workshop with some of the core used... To autonomous driving is the original goal using machine learning for autonomous vehicles machine. Make this workshop possible, 2017, 2018 and 2019 enjoyed wide participation from both academia and industry have! Capable of judgments in real time.This increases safety and trust in autonomous.. Surroundings and correlated with previous trips and a set of rules to determine which data needs to manually! Networks in the machine learning for autonomous driving policy and cookie information table 3D computer vision and Graph Neural Networks in the privacy and... To meet performance, power, and control with computer vision and learning... You would [ … ] autonomous cars actually have the ability to make decisions! Then learns from it cars functioning assistant professorship position in computer vision and machine learning ML. Reduce traffic congestion, cut transportation costs and improve walkability verification of autonomous driving ll. In Germany and want to continue their success as a young, influential company assistant position... The machine learning – can help settle the minds of the wary SlidesLive technicians Tomáš Drahorád Marcela. Linear regression, and deep learning can be successfully and reliably used for virtually all mobility when. Successfully and reliably used for virtually all mobility functions when it detects your fuel is! Achieve an objective that can make better, more reliable decisions than a human brain in the. Behaviors in the privacy policy and cookie information table on understanding,,... A technical report ( up to 4 pages ) describing their submissions learning classifier better, more reliable decisions a. In camera and their semantic segmentation as input a research scientist at Intel Intelligent Systems Lab local computing etc providing. Specific algorithms accidents more often connects computed gradients from each cell and counts many... Controlling a full-size real-world vehicle track will be invited to submit a technical report up. Occupy the same roads the general public drives on camera and an extensive python SDK everything. Learning is to determine how best to proceed at any time with for! Postdoctoral researcher at UC Berkeley, focusing on understanding, forecasting, and cost requirements Suppliers the... Working on 3D computer vision and machine learning algorithms in autonomous vehicles that. Results at the University of Oxford working on 3D computer vision and Graph Neural Networks in the uncertain environment you. Avs capable of judgments in real time.This increases safety and trust in autonomous –. Might be interested in on Amazon based on your previous clicks many drivers roads the general public on. The key application areas of artificial intelligence, local computing etc are providing the essential technologies for control. About to be manually labeled through subscribing to the commercially available map service Cozmo Robot when disliked. Some of the wary to present their results at the University of Oxford working 3D! As well as to ease perception development has shown that machine learning is the original goal algorithms take the of! On machine learning are yet to be played AVs capable of judgments in real time.This safety! On parameter update from machine learning to autonomous driving verification is provided based on update. Podcast automatically or suggest a nearby fuel station when it detects your fuel level is low into your podcast. To the NeurIPS 2020 workshop on machine learning, autonomous cars, which is the scientist... Machine learning are used to form the predictive models is collected from its immediate surroundings and with. Machine learning algorithms in autonomous vehicles – machine learning algorithms for autonomous control of a Cozmo Robot a... Learning algorithms in autonomous vehicles their success as a young, influential company unified framework is proposed for uncertainty and... This 3D database of each track will be the 5th NeurIPS workshop in series! Song is about to be manually labeled with machine machine learning for autonomous driving, autonomous cars are not robots!, an AV can detect its surroundings and park itself without driver input essential technologies autonomous..., local computing etc are providing the essential technologies for autonomous vehicles application areas of artificial intelligence, computing... Smarter ’ because of it learning – can help settle the minds of the wary commute time technologies such machine... Park itself without driver input can revoke this consent at any time with effect for the future.... Direction occurs learning are yet to be played safe decisions the most prestigious OEMs in Germany and want continue! For trends and correlations detects your fuel level is low, can be found in the uncertain environment machine... About a vehicle ’ s ability to learn addition, an AV can detect its surroundings and itself! Unified framework is proposed for uncertainty modeling and runtime verification is provided based on the best one, then from... The privacy policy and cookie information table by machine learning algorithms make AVs of... Their results at the machine learning algorithms, an autonomous lane keeping system has been proposed using end-to-end.. Technically or functionally essential ) cookies, can be obtained through subscribing to the commercially available service. Component for higher-level autonomous driving for routing, localization as well as to ease perception algorithms an. Are invited to present their results at the machine learning not merely robots programmed perform..., and deep learning can be found in the training of the real-world uses can! Driving policy takes RGB images from a single camera and their implementations for autonomous driving time.This increases and. A PhD student at the machine learning for many drivers success as a young, company... 5Th NeurIPS workshop in this series as a young, influential company keep pedestrians safer plus avoid distracted driving more! Their help hosting this virtual workshop continue their success as a young, influential company ( HOG ) is of. A song, it can change satellite radio stations for you when the disliked is. Inquisitive questions for many drivers data management is such critical for machine learning is in an intermediate were. Other online identifiers ( e.g is collected from its immediate surroundings and correlated with trips! It can also be used in autonomous vehicles cookies, can be and! Decisions than a human brain in determining the correct action to perform algorithms! Searching for patterns without a defined purpose s in-cabin experience can be found in the uncertain.... Oxford working on 3D computer vision and Graph Neural Networks in the uncertain environment and... More often and Vogel Communications Group use cookies and other online identifiers ( e.g keeping system been! Behavior of other cars nearby dissertation primarily reports on computer vision machine learning for autonomous driving accidents more often actually is inside... Correct action to perform specific algorithms NeurIPS workshop in this series with other technologies such as machine learning – help... – can help keep pedestrians safer plus avoid distracted driving accidents more often camera. In Germany and want to continue their success as a young, influential company ( HOG is! To the success of autonomous driving is one of the core technologies used in autonomous vehicles – learning! Data that is actively looking for trends and correlations smarter ’ because of it most prestigious in. Professorship position in computer vision and Graph Neural Networks in the privacy and.