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, last year's program featured two days of sessions and panel discussions addressing challenges from L2-L5 autonomy.
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.
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.
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.
Ron 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.
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
Improving vision systems precision and performance, challenges from L1-L5
Roxie Paine | Level 5 Hardware Technical Project Program Manager of Lyft
Abstract To Come
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.
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.
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.
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:
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
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.
What challenges must the industry overcome from each end of the supply chain to reach level 5 autonomy.
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.
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.
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.
The impact of recent accidents and their effect on the market.
Miguel D. Acosta | Chief Of The Autonomous Vehicles Branch of California Department of Motor Vehicles
Alex Epstein | Director of Transportation Safety of National Safety Council
Abstract to come
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
What challenges are there and how are they being met. Sensor performance and improvement, needs challenges
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.
Anand Gopalan | Chief Technology Officer of Velodyne LiDAR, Inc.
Abstract to come
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.
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.
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.
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.
Panelists include AEye, ImmerVision, and SensL