2018 Agenda | Image Sensors Auto Americas

Get the latest on imaging and auto sensors, autonomous vehicles, and AI and more.

Focusing on the theme, The Road to Autonomy and Everything In Between, this year's program will feature 2 days of sessions and panel discussions addressing challenges from L2-L5 autonomy. 

October 9, 2018 | Day 1

Registration and Opening Remarks

  1. Registration Opens & Continental Breakfast

  2. Welcome & Opening Remarks

Session I | Market Overview: Autonomy, The Big Picture

This session will provide an update on the current progress of autonomous driving and provide projections for the future of the industry identifying challenges and trends. 

  1. Self-Driving Cars: Massive Deployment of Production Cars and Artificial Intelligence Evolution

    Junli Gu | VP of Autonomous Driving of XMotors.ai

    Autonomous driving has gained enormous attention and momentum in recent years, due to its huge impact on transportation industries. This talk will share the perspective of XMotors, including strategies for massive deployment of self-driving solution, key insights on artificial intelligence evolution, personalized and geographically differentiated driving features.

  2. Sensing the Scene From a Heavy Duty Vehicle’s Perspective

    Nazre Batool | Development Engineer Automotive Transport Solutions Pre-Development & Research of Scania

    At Scania, our heavy-duty autonomous vehicles have endeavored to navigate in a variety of environments, from isolated mines to urban roads, autonomously. The adaptation of contemporary sensing technologies and sensor data algorithms to match our vehicles’ specific needs to reach these autonomous driving milestones has not been easy. The talk will include Scania’s experiences and specific challenges faced in this endeavor.

  3. Industry Update, Market Overview

    Ronald Mueller | CEO of Vision Markets of Associate Consultant of Smithers Apex

    From high dynamic range to cyber security to single photon sensitivity for LiDAR – image sensors for automotive are facing major challenges in light of requirements of autonomous driving. Smithers Apex has conducted a detailed market analysis of the automotive image sensor market and ranked the challenges in a balanced score card taking into account their impact on the market and their technical maturity. The talk outlines the contents of the market study and denotes the trends which are pushing the developments at material researchers, sensor OEMs, foundries, and supporting technology suppliers.

  4. Panel: Industry Needs

    Moderated By Waymo | Panelists Include Byton and Xilinx

    Innovation & Challenges. OE, Tier 1, and industry analyst discuss challenges they face as they work toward improving image sensors for automotive applications. From in cabin monitoring to their innovations wish list…hear their perspective and industry outlook.

    Panelist to be confirmed

    • Zhenhua Lai,Lead Imaging Engineer, Byton
    • Roger Melen, Senior Advisor, Toyota InfoTechology Center 
    • Will Tu, Senior Director Automotive, Xilinx
    • Marcus Behrendt, Partner, BMW i Ventures
  5. Morning Networking Break

Session II | Advanced Image Sensor Technology

Improving vision systems precision and performance, challenges from L1-L5

  1. Image Sensors Providing Sight for the Future of Self-Driving Vehicles

    Roxie Paine | Level 5 Hardware Technical Project Program Manager of Lyft

    Abstract To Come

  2. MIPI CSI Imaging Conduit Development

    Haran Thanigasalam | Senior Architect & Independent Contributor (Intel) Chair of Imaging and Camera Workgroup of MIPI Alliance

    MIPI CSI-2 is rapidly advancing to meet the needs of emerging imaging and vision applications targeting multiple platforms including automotive. This presentation will focus on CSI-2's key features, capabilities, and select use cases mapped to product platforms. In addition, highlight new features presently in development for EOY 2018 and EOY 2019 releases.

  3. Image Sensor To Enhance Safety of Autonomous Driving

    Tsuyoshi Hara | Automotive Solution Architect of SONY electronic

    "Safety Cocoon" is a name expressing the creation of an area surrounding the car where a car can detect its surroundings in 360 degrees and prepare for danger avoidance from an early stage in various driving situations, enhancing safety in our society of constantly improving levels of autonomous driving. Adopting Sony's image sensor will contribute to achieving enhanced safety efficiently. The "Safety Cocoon" concept is explained by comparing the visibility of human, car and image sensor.

  4. Eye Tracking/Driver Monitoring

    Kenneth Funes Mora | CEO and Co-founder of Eyeware

    Virtual co-pilots will be required to sense the driver's attention, for monitoring purposes and controlled handovers, to minimize human error. However, they will also use the eye gaze to allow a more natural interaction with in-cabin infotainment solutions.

    Eyeware is proposing a new approach to solving the eye tracking problem using autograde depth cameras.

  5. Afternoon Networking Lunch

Session III | Computer Vision and Deep Learning

  1. Edge Mapping With Minimal Footprint, Power, and Cost

    Sravan Puttagunta | CEO and Co-founder of Civil Maps

    The next evolution in AV mapping and localization will occur at the chip-level as developers move from expensive, power-hungry systems to alternative architectures that are widely available and deployable with minimal cost and power consumption. This gives rise to the techniques involved in Edge Mapping, the real-time ability to create, update, and share HD maps in-vehicle.Edge Mapping pioneer Civil Maps will share how it has integrated with Arm’s architecture, licensor to the largest ecosystem of auto electronic systems, to achieve these goals. The company will also present road test scenarios such as urban, rural, tunnel, off ramp, and highway, where their edge-based approach was applied.

    ●          Sravan will leverage real-world case studies and data points to illustrate the limitations of traditional approaches and advantages of edge-based technology for mapping and localization.

    ●          Arm's processors incorporate machine learning capabilities that are currently used for speech recognition and computer vision. Sravan will outline why these CPUs make a natural choice for autonomous vehicle control, including:

    • Low power requirements
    • Greater system reliability
    • Smaller hardware footprint
    • High performance
    • Low cost
    • Arm ecosystem
  2. Deep Learning Based on Visual Perception for Surround View Camera Systems

    Sumanth Chennupati | Computer Vision Systems Engineer R&D of Valeo North America

    Deep Learning offers solutions to highly complex visual challenges like classification, localization, and segmentation. Extraordinary performances on semantic segmentation, object detection etc., provides autonomous vehicles critical information about drivable areas, surrounding obstacles’ location and speed etc. The majority of the research has been done using single camera images. However, learning from multiple cameras to understand the surrounding will offer advantages in some critical cases. This presentation will review

    • Latest trends in designing and training deep learning architectures
    • Advantages of using Surround view cameras
    • Good design strategies in building datasets for learning
  3. Intelligent Vision for Automotive

    Patrice Roulet Fontani | Vice President,Technology and Co-Founder of ImmerVision

    Assisted and self-driving systems have generally reached the point of being able to analyse their environments and process decisions but are held back by the lack of adequate perception. The move from driver convenience to ADAS and full autonomy requires a truly Intelligent Vision system that includes perception suitable to humans and machines, and processes data captured through sensor fusion. The system properties must include low-latency, reliability, low-power consumption, and cross-platform support.  High-resolution, wide-angle lens technology and tunable image processing, combined with sensor fusion technology, addresses capturing, channeling, synchronizing, and contextualizing the data from the multiple sensors. In this talk, we will explore the key roles of reliable, AI-classifiable, and actionable pixels and the lens and sensor technology required to capture them.

  4. Closing Remarks For The Day

  5. Evening Networking Reception

October 10, 2018 | Day 2

Welcome and Opening Remarks

  1. Registration Opens

  2. Opening Remarks for the Day

Session IV | The Road to Autonomy

What challenges must the industry overcome from each end of the supply chain to reach level 5 autonomy. 

  1. Keynote Presentation: Sensors on the Road: Deploying Autonomous Vehicles in Urban Environments

    Steve Vozar | Chief Technology Officer & Co-Founder | May Mobility

    In June 2018, May Mobility launched the first commercial deployment of self-driving vehicles in the United States. Shuttling passengers in downtown Detroit in mixed traffic on public roads, the service has provided more than 10,000 rides as of Sept 2018. May Mobility’s CTO, Dr. Steve Vozar will talk about the road that May took to get from founding to deployment in just over a year, and how our unique, targeted strategy enables us to bring self-driving transportation to everyday people faster than companies with bigger checkbooks and more staff.

  2. Achieving Mass Commercialization: What will it take for autonomous driving to go mainstream?

    Aditya Srinivasan | Innoviz of General Manager, North America

    Fully autonomous vehicles are coming in the not-so-distant future, and it won’t be long before self-driving cars are available to everyone. But what will it take for autonomous driving to go mainstream? What technology is necessary to make it happen? How can the industry make prices reasonable for the masses? What obstacles stand in our way, and how can we overcome them? In this session, Innoviz CEO, Omer Keilaf, will answer these questions as others as he presents a roadmap for achieving mass commercialization of autonomous vehicles. 

  3. The M&A and Funding Landscape for Image Sensor Companies

    Rudy Burger | Managing Partner of Woodside Capital Partners

    Financial transactions (both M&A and private placements) have continued over the last few years driven by both the smart phone and autonomous vehicle markets and the continued drive for lower power consumption, improved low light and infrared performance, and specialized computer vision applications.  This presentation will examine the key acquisitions and investments for image sensor companies over the last few years and suggest a few key trends that will likely be driving financial transactions for image sensor companies in the years to come.

  4. Morning Networking Break

Session V | Challenges in Safety and Regulations

The impact of recent accidents and their effect on the market. 

  1. Future Outlook for Autonomous Driving Industry: Regulations and Safety

    Miguel D. Acosta | Chief Of The Autonomous Vehicles Branch of California Department of Motor Vehicles

    Abstract To Come

  2. Road Safety and Autonomous Driving

    Alex Epstein | Director of Transportation Safety of National Safety Council

    Abstract to come 

  3. Liability Issues in the Age of Autonomous Vehicles

    Chad Mowery | Partner of Roetzel and Andress

    Autonomous vehicles will not only revolutionize our roadways, but lead to vast changes in the liability landscape surrounding automobiles.  From product liability matters for original equipment manufacturers to inquiries on how component manufacturers integrate their products into a vehicle to questions about consumer liability for vehicle accidents, the methods of how participants at all levels of this market address these issues will change dramatically with increases in automation and connectivity between vehicles.  This presentation will look at these liability issues as well as how manufacturers, maintenance providers, consumers, and insurers can begin to prepare for the changes autonomous vehicles will bring

Session VI | Sensor Innovation

What challenges are there and how are they being met. Sensor performance and improvement, needs challenges

  1. Active Alignment Optical Assembly and Test Solutions for Today’s and Tomorrow’s Automotive Cameras and LiDAR Sensors

    Dr. Daniel Winters | CEO of Trioptics

    Today's automotive sensors like cameras or LiDAR systems have to work under stringent environmental conditions like e.g. high and low temperatures or strong vibrations. As the industry moves to more sophisticated sensors with higher resolution, error budgets during assembly become smaller, sensors become more sensitive to misalignment and need more accurate calibration.

    State-of-the-art active alignment technology helps overcome these challenges by aligning optical elements and sensors individually in up to 6 degrees of freedom with micron-level precision. In combination with precise calibration this improves production yields and gives more consistent and better quality than conventional assembly techniques.

  2. Velodyne LiDAR

    Anand Gopalan | CTO of Velodyne LiDAR, Inc.

    Abstract to come

  3. Afternoon Networking Lunch

  4. Moving From Legacy LiDAR to Next Generation Perception

    Dr. Allan Steinhardt | Chief Scientist of AEye, Inc.

    The industry is shifting from raster scanning LiDAR technologies to intelligent vision systems that dynamically perceive a scene. First generation LiDAR systems, because of siloed sensors and rigid data collection methods, tend to oversample or undersample information. This requires significant processing power and time to extract critical objects, leading to latency. New systems are emerging that fuse intelligence with the data collection process - enabling the dynamic tracking of targets and objects of interest, with almost no computational penalty. AEye will delve into this new form of intelligent data collection, called "iDAR", and its role in delivering human-like perception. Highlight the differences between legacy LiDAR systems and emerging intelligent vision systems - Discuss the differences in data collection methods - Explain how dynamic tracking works - Discuss iDAR's role in optimizing data collection, reducing bandwidth, improving vision perception and intelligence, and speeding up motion planning for autonomous vehicles. 

  5. Laser Diode Solutions for Automotive LiDAR Systems

    Brandon Collings | CTO of Lumentum

    A variety of diode laser illuminators have been adopted in different LiDAR systems to cover demanding performance requirements. There are two types of diode lasers: vertical-cavity surface-emitting laser (VCSEL), and edge emitters. Each of these lasers has characteristics which are ideal for specific tasks. Diode laser illuminators of both types, developed by Lumentum over the last several years, have been adopted and advanced in the consumer electronics market. Reliability, and the ability to manufacture in volume with high quality are critical for the demanding consumer and automotive markets. We will present advancement of high power VCSEL array illuminators, manufactured in large volume production lines. Test results demonstrate these illuminators can reliably operate in the temperature range -40C to 115C, and under other environmental stresses required for automotive-qualified products. For an edge-emitting laser, we will introduce a new 1.5um tunable Distributed Bragg Reflector (DBR) laser prototype which will enable frequency modulated continuous wave (FMCW) Coherent LiDAR for >150m range.

  6. Co-Developed Sensor Fusion for Automotive Applications

    Carl Jackson | CTO and Founder of SensL Division, OnSemi

    The fusion of radar, LiDAR and camera data is a key trend for the future of autonomous vehicles. ON Semiconductor, through the acquisition of SensL Technologies, is now able to provide a LiDAR solution capable of long distance ranging with low reflectivity targets, through the use of their SPAD and SiPM technology. This combined with ON Semiconductors radar and image sensor expertise and experience in the automotive market, can be leveraged to co-develop the technologies from the outset to facilitate the fusion of sensor data. This presentation will highlight the ON Semiconductor vision for this fusion, leading to improved capabilities for the automotive market.
    Full vehicle autonomy will be achieved through sensor fusion - ON Semiconductor's acquisition of SensL gives it the distinctive position of being able to provide solutions for radar, image sensors and now LiDAR for this market. - The co-development of the technology for these modalities will allow for a more robust fusion of the data.

    • Full vehicle autonomy will be achieved through sensor fusion
    • ON Semiconductor's acquisition of SensL gives it the distinctive position of being able to provide solutions for radar, image sensors and now LiDAR for this market.
    • The co-development of the technology for these modalities will allow for a more robust fusion of the data
  7. Shifting Next Generation Radar into High Gear

    Ram Machness | Vice President, Product of Arbe Robotics

    The autonomous driving industry requires a reliable sensor that performs in all lighting and weather conditions. In addition, in the crowded autonomous environment where cars may drive one towards the other on highways at high speeds, next to bike riders or stationary objects, the sensor must be able to “see” them coming from 300 meters away, separate between them in real time, track velocity, and measure distance.

    In this presentation, Ram Machness, will explain how a 4D imaging radar can overcome these challenges. He will focus on the technical aspects of achieving low false alarm rates, coping with mutual radar interference, providing high refresh rates, while keeping prices low and accuracy high.

  8. Panel Discussion

    Panelists include AEye, ImmerVision, and SensL

    Panelists include:

    • Dr. Allan Steinhardt, Chief Scientist,  AEye
    • Dr. Carl Jackson VP Engineering,  SensL
    • Patrice Roulet, Technolgy and Co-Founder,  ImmerVsion
  9. Conclusion of the conference