Naghmana Majed CTO, global automotive and aerospace and defense industry IBM USA
The presentation will outline a solution using blockchain for OTA software updates to vehicles, enabling traceability, auditability and compliance. It will also discuss how we can enable OEMs to move toward an Open Vehicle platform around OTA software updates and new features, and look at how to reduce testing and deployment cycles.
Artificial intelligence in the driver’s seat
Serkan Arslan Director of automotive NVIDIA EMEA GERMANY
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.
Progress on the AUTOSAR adaptive software platform for intelligent vehicles
Dr Günter Reichart Spokesperson AUTOSAR Development Partnership GERMANY
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 priority.
10:30 - 11:15
Challenges of deep learning in the automotive industry
Dr Florian Baumann CTO Automotive & AI Dell EMC GERMANY
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: data collection and acquisition phase, data annotation phase, quality checks and finally constructing the network with its test and validation in 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; understand how many miles must be driven, how many images annotated and the massive investment in terms of effort for test and validation.
Deep learning for automation and quality control in crowdsourcing applications
Bernd Heisele Principal vision engineer Mighty AI USA
Autonomous vehicles must be trained to detect and avoid road obstacles with incredible accuracy to safely operate on public roads. Efficiently generating precise manual annotations for ground truth training datasets is a top priority for computer vision teams who must label thousands of frames in a timely and cost-effective manner. Although crowdsourcing these annotations delivers necessary scale, the results can be insufficient without proper quality control. In this presentation, we discuss a novel approach to quality control in crowdsourcing applications, which uses state-of-the-art deep learning model (Faster RCNN NASnet) to predict performance and provide real-time feedback to human annotators.
Presentation title and synopsis TBC
Dan Cauchy Executive director of Automotive Grade Linux The Linux Foundation USA
12:45 - 14:00
Tuesday 21 May
Afternoon Session 14:00 - 17:45
AI and big data management for autonomous driving
Frank Kraemer Systems architect IBM GERMANY
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.
Autonomous driving Level 5 – percept and fusion
Davor Andric CTO AI and analytics North and Central Europe DXC Technology GERMANY
The presentation will cover: (1) Analyzer – distributed/parallel processing of automotive formats on sensor level without data conversion and storage duplication, reduced time to analyze --> bring algorithms to the data; (2) Optimizer – custom annotation and extraction of AI relevant data sequences; (3) Trainer – state-of-the-art autonomous driving platform for object recognition, fusion of disparate sensor data sets and formats; orchestration of AD/ML workloads.
Practical validation of AI safety within the ISO26262-6 framework
Dr Edward Schwalb Lead scientist MSC Software USA
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.
15:30 - 16:15
Seeing through clutter: compressive learning for single photon lidar
Puneet Chhabra Co-founder and CTO Headlight AI UK
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.
Automate and validate AV algorithms with large driving sensor datasets
Nicolas du Lac CEO Intempora FRANCE
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 datasets, 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 architectures scalability, while testing your features against relevant data only.
HPC, artificial intelligence, virtualization – a creative combination for ADAS development
Gianluca Vitale Global business segment manager AVL List GmbH AUSTRIA
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.
Please Note: This conference programme may be subject to change