application frameworks architect
Synopsis: Linux promises to be a feasible and open operating system solution for autonomous driving platforms. With a broad palette of libraries and tools, typical machine learning and computer vision algorithms are easily developed and tuned for performance and throughput. However, functional safety requires isolated and time-critical execution of certain applications and tasks. Is Linux still a feasible choice? This talk recaps the basic resource management and scheduling mechanisms on OS and middleware (e.g. Adaptive AUTOSAR) level. Given artificial examples, it emphasizes application and system design challenges, from a theoretical perspective, and has an impact on systems dynamics and overall utilization.
chief executive officer
ANYVERSE (Next Limit)
Synopsis: Training self-driving technology is a crucial step in autonomous vehicle development and deployment, especially in terms of the much-scrutinized safety issue. Unfortunately, this part of the process is still facing a major data challenge. The real-world approach has proved to be insufficient and time-consuming, slowing down progress and exposing it to numerous loopholes. An alternative solution is the use of virtual images that meet specific training and testing needs and complement real-world data. However, synthetic data is all not made equal. It needs to be as real and physically accurate as possible, apart from including all segmentation data. This is ANYVERSE.
AUTOSAR Development Partnership
Synopsis: The AUTOSAR community has grown to approximately 250 partner companies since the first AUTOSAR classic platform specification was released more than 15 years ago. AUTOSAR adaptive platform is a completely new approach to cope with the challenging market trends in the automotive industry, such as internet access in cars, highly automated driving and V2V communication. The platform runs on high-performance computing hardware and supports parallel processing on many core systems and GPUs. Consequently, it can also be used to support high-bandwidth communication and is able to host AI applications. AUTOSAR has its roots in the automotive industry with safety and security as the highest priorities.
global business segment manager
AVL List GmbH
Synopsis: The automotive industry is undergoing the biggest shift since cars hit the roads. New electrification and autonomous technologies are combined with the conventional ones, generating even higher system complexity than before. Indeed, increasing model complexity creates a challenge for virtual testing as the number of test cases increases exponentially. An alternative to brute force testing is to restrict the procedure to execute only the most relevant test cases by exploiting artificial intelligence methods. In the present approach, we introduce a strategy that utilizes the power of cloud-based scaling controlled by AI algorithm to filter and pinpoint critical scenarios.
Bird & Bird LLP
Synopsis: Developing AI solutions poses a multitude of challenges. From the regulatory framework on AV, implementing privacy by design and default, ensuring functional safety, testing the solutions in 'sandbox conditions', addressing contractual and product liability, all the way up to dealing with the critical ethical issues (dilemma situations), developing solutions requires a conscious, well-prepared approach. Although 'try as you go' has never been the approach of the industry, the legal requirements on process and proper documentation, as well as managing risk, are critical success factors for AV. The presentation sets out the key issues and discusses possible solutions.
chief scientist and co-founder
Synopsis: Perception, whether camera- or lidar-based, is the heaviest task in L4/L5 autonomous vehicles. Although the first robot-taxis and luxury vehicles may not be as sensitive to cost, it is important to understand what the options are for the mass production of autonomous vehicles. The presentation will discuss the improvements possible within deep learning algorithms that will enable the mass production of autonomous vehicles. We will review deep learning frameworks, inference engines – including whether or not to write your own – and neural network optimization. Throughout the presentation we will share measured data and results for every step in the chain.
CTO automotive & AI
Synopsis: After briefly introducing deep learning, the talk will focus on the common workflow of constructing a neural network in terms of the lifecycle of automotive product development: the data collection and acquisition phase, data annotation phase, quality checks and finally constructing the network with its test and validation as a last step. The audience will understand the common workflow and the basics of constructing a deep-learning-based classifier for automotive product development; become aware of typical challenges/problems and how to avoid and counter them; learn how to test and validate deep-learning-based algorithms for autonomous driving; and understand how many miles must be driven, how many images annotated, and the massive investment needed in terms of effort for test and validation.
CTO AI and analytics North and Central Europe
Synopsis: The vision for fully autonomous vehicles has yet to be realized. How realistic is it? This session will review current approaches and challenges for autonomous driving development, including human driver behavior, and examine what is needed to develop autonomous driving technologies for intelligent and real-time action.
CTO and co-founder
Synopsis: A vehicle’s physical capabilities are crucial for the feasibility and smoothness of any maneuver. Traditional motion planning methods for AD neglect most of the physics, being conservative or requiring advanced low-level vehicle controls that are often not present or are prohibitively expensive. We demonstrate physics-based motion planning technology, using numerical optimization, to calculate smooth and safe trajectories which can be easily followed by standard low-level vehicle controllers. Based on recent advances in embedded optimization technology, we capture most of the relevant vehicle dynamics while driving on a highway or on rural roads, significantly extending the performance envelope of autonomous cars.
Foresight AI Inc
Synopsis: In this presentation we will share our experience and lessons learned in building data processing pipelines for autonomous driving and related AI technology development. We will dive deeply into multiple components of the data processing pipeline, including data acquisition, ingestion, selection, annotation, visualization and retrieval. We will showcase several software tools, with web-based user interface, algorithm enhancement, task management and quality assurance. Based on our data pipeline, we have enabled efficient training of deep-learning-based object detection, 3D high-definition map generation and 3D object tracking. We will also discuss several hot topics, such as using algorithms to select and annotate data, enabling data traceability, etc.
group leader - digital assurance
Frazer-Nash Consultancy Ltd
Synopsis: Machine learning (ML) is making rapid progress in a variety of applications. It is highly likely to be used in safety-related and possibly safety-critical systems. There is a need to consider how to make safety arguments for systems that exploit AI techniques; more generally, there is a need to make safety arguments for autonomous systems that make use of them. This paper presents the work undertaken by a consortium led by Frazer-Nash Consultancy in support of the Defence Science and Technology Laboratory to determine the types of safety argument.
co-founder and CTO
Synopsis: Sensors are vital for a wide range of key markets, including transportation, utilities, mining, security, construction and manufacturing; and they will remain a fundamental building block of the future. However, they are limited in their applications to ideal conditions. They don’t see effectively when they most need to – in fog, heavy rain or in complete darkness – and it is challenging to process the vast quantity of data they generate. This presentation introduces Headlight AI’s latest development in the real-time processing of multi-return, multi-spectral lidar and radar measurements using compressive deep learning techniques.
Synopsis: Advanced driver assistance systems (ADAS/autonomous driving) are becoming part of all vehicles. All major OEM and Tier 1 auto manufacturers are implementing and testing AD facilities. We examine how real-time sensors, big data computing, data storage and data archiving are integrated in today's ADAS/AD systems, providing a fascinating case study of best practices for workflow design, testing and development; data storage; and archiving, applicable to all industries.
Synopsis: It can be regarded as a fact that the complexity of environment perception in highly and fully automated driving – and very likely also the trajectory planning – requires a learning AI system. Today, machine learning/deep learning algorithms lack explainability and robustness, which compromises functional safety. The presentation will explain some approaches to overcome these drawbacks and describe current work at iMotion Germany in collaboration with the German Research Center for Artificial Intelligence.
Nicolas du Lac
Synopsis: With diversity of scenarios and the unpredictability of perception algorithms, it becomes necessary to perform statistical validation as formal proof becomes too complex and unaffordable. Costs for such validation can be tremendous. How to manage petabytes of recorded sensor data sets, unaltered videos and a growing number of computing nodes? We will introduce an innovative software suite that can help to benchmark, test and automate the validation of ADAS and HAD functions. It allows computing resources to be optimized for storage and high-performance computing by taking advantage of software execution performance and cloud architecture scalability, while testing your features against relevant data only.
CEO and co-founder
Synopsis: Safety is the main obstacle limiting the application of autonomous systems. A crucial factor contributing to the overall safety of an autonomous system is the software for high-level decision-making behavior. Defining autonomous decision-making behavior and guaranteeing that the behavior is safe is a complex endeavor. In this talk, we present an innovative approach combining techniques from automated planning, formal verification and constraint programming in a systematic process that helps software engineers specify and get guarantees on high-level decision-making behavior.
head of functional safety - autonomous driving department
KPIT Technologies GmbH
Synopsis: Applications based on artificial intelligence are improving at a rapid pace. The accuracy of the inferences is constantly increasing. However, a small percentage of wrong inferences remains. Those mispredictions are very hard to detect. From the perspective of functional safety, errors that cannot be detected are unacceptable. Nevertheless, techniques can be used to avoid and detect mispredictions. This presentation is structured around the following points: the notion of confidence in the inputs to the AD, the safety mechanisms working on the spatial surroundings of the vehicle, and the temporal sequence of events perceived by the vehicle.
EMEA HPC and big data architect, autonomous driving platform solutions
UNITED ARAB EMIRATES
Synopsis: Autonomous driving is fundamentally transforming the transportation industry, with computer vision, AI and HPC leading the change. As new, highly autonomous mobility programs launch, customers are struggling to scale their operations and are beginning to realize they need to partner with cloud providers. The data streams generated from these vehicles is unprecedented, resulting in the need for massive scale across the entire workflow: from PB-scale data ingest, to storage, algorithm validation, training, simulation and validation using tens of thousands of CPU cores and thousands of GPUs. Learn how to position Azure to be the most trusted, secure and scalable platform (cloud and edge) for developing and testing autonomous vehicles.
Synopsis: It is often mentioned that ISO 26262-6 is inadequate for addressing safety validation of autonomous vehicles comprising AI components. As an example, there is a need to validate behavior when faced with unprogrammed objects, e.g. a piece of tissue paper vs. aircraft on the road; some refer to this as the 'category problem'. In this presentation we review the various relevant sections of ISO 26262, including 8.4.2, 8.4.5, 9.4.3, 9.4.4, 9.4.5, 10.4.3, 10.4.4 and 10.4.5. We provide a practical approach to address key aspects of the 'category problem' within the ISO 26262 framework.
senior director product management
MSC Software GmbH
Synopsis: The number of tests/events to verify an autonomous system is enormous. In addition, the static and dynamic environment must be considered much more explicitly in the test. This can only be done by virtual simulation. It is important to ensure that all relevant tests are performed, considering all eventualities. Such large and complex test plans can only be built with the help of artificial intelligence.
director of automotive
Synopsis: New data center infrastructure and in-vehicle supercomputing platforms are required, and AI is being used to process data from cameras, radars, lidars and other sensors. Algorithms leveraging structure from motion, sensor fusion and deep learning will help perceive the environment, create HD maps, predict traffic and behavior and then control traffic. To ensure AVs are safer than human-driven vehicles, developers need to drive millions if not billions of miles to properly train and test them. Combining photo-realistic simulation and AI enables the industry to safely drive billions of miles in virtual reality – testing an unlimited variety of conditions and scenarios.
director of business development
Synopsis: The emergence of lidar as a critical 3D sensing modality for autonomous vehicles has resulted in a need for computer vision scientists to develop new algorithms to segment, track and classify point clouds. Progress has been limited by the inability to apply decades of methodologies from camera-based vision due to the novel data formats and structures that conventional lidar output. Recent breakthroughs in lidar hardware enable camera-like imagery of both ambient and signal data in a rectilinear, camera-like grid. This talk will focus on the resulting implications for deep learning, and will feature applications of camera deep learning algorithms on lidar.
training data specialist
Synopsis: Does it really require infinitely more training data to get your model to 100%? Getting an algorithm to 99%+ accuracy often feels like approaching the speed of light. Although some applications of AI are okay with sub-100% thresholds, anything less than 100% just simply won’t cut it for applications where lives are at stake, (e.g. pedestrian detection). This talk will investigate the emerging best practices derived from 75+ autonomous vehicle projects around breaking free of the 'subluminal' data barrier, and address questions such as does a 'warp drive' to 100% accuracy exist or is there a long, incrementalist slog ahead?
Alexander Van Bellinghen
Siemens Industry Software NV
Synopsis: The introduction of automated driving vehicles leads to increased complexity in automotive software. This paper explains how a formal contract-based software design and testing approach based on an executable requirements model front loads the implementation, validation and verification of ADAS/AV software. Requirements are transformed into engineering contracts that are put on top of the software architecture to ensure architecture consistency, drive the software implementation specification (C/Simulink/…) and channel unit or integration testing. This contract-based design methodology considering requirements as engineering contracts will be explained through an adaptive headlight software use case.
Synopsis: Given its complexity, the testing of Level 3 and 4 highly automated driving has become the bottleneck. In this presentation, we define scenarios and consider why they are important. First, we undertake scenario identification to determine what is relevant and what should be simulated. Second, we automatically extract observations from multiple sources and highlight the advantages of observation-based extraction over scenario creation. Third, we take the important step of fuzzing and pruning by combining relevant scenarios in a meaningful way. Different use cases and ODDs require different fidelities and different simulation variants. We end our presentation by giving a live demonstration of our scenario pipeline.
assistant professor, computer science
University of Virginia
Synopsis: What will an AV do if another vehicle swerves across multiple lanes without any indication? Or when the car in front brakes without warning? Or an obstacle appears at the last second in front of the car? How do we ensure that the car drives safely and reliably in situations that don’t happen often in day-to-day driving and are therefore difficult to gather data on? This talk will describe the research being done at the UVA Link Lab, where we teach AVs to learn how to deal with edge cases in traffic by being agile.
Synopsis: According to ISO 26262, software components and libraries have to be classified into 'unchanged' or 'modified/new' components. New/modified libraries have to be developed according to ISO 26262. However, for usually unchanged libraries, like C/C++ standard libraries or C runtime libraries, there is a simplification in ISO 8-12. In the talk we present the testing requirements for software libraries and how they can be qualified. In addition we present the Validas growing qualification kit for C/C++, which already covers about 200 C library functions and can be used to qualify many others.