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The Race to a Low-Cost Automotive LIDAR System
Phil Amsrud
Senior Principal Analyst
IHS Markit

We’ve been publishing deep research about automotive LIDAR since 2017. This year, we’ve refreshed and expanded our research. LIDAR sensors are considered part of the standard ADAS/AD suite for most OEMs to complement and provide redundancy to cameras and radar sensors. However, there are several competing LIDAR technologies and cost remains one of the obstacles to wider adoption of LIDAR sensors. Currently there are as many as 100 companies working on LIDAR systems including OEMs, Tier 1s, established LIDAR suppliers, and many start-ups. In this presentation, we will provide an overview of the various LIDAR technologies, the supply chain, commercial aspects, and the potential for each LIDAR technology type. Further, based on our research, we will highlight and benchmark different LIDAR implementation approaches in terms of cost, maturity timeline, and integration with other types of sensors. We will also provide a review of the current LIDAR supply chain ranging from established suppliers to some of the most promising start-ups.

Biography: Phil Amsrud is a senior principal analyst for the IHS Markit's automotive semiconductor research area with a special focus on advanced driver-assistance systems (ADAS) and autonomous driving technologies. Phil began his career in automotive electronics as a design engineer at GM on their ABS systems. From there he joined Motorola Semiconductor Products Sector supporting Delphi Electronics, which became part of Freescale Semiconductor that was spun off from Motorola. At Freescale he managed the field sales and applications engineers supporting Continental Automotive. After obtaining a Master’s degree in business, Phil joined ON Semiconductor and was responsible for field sales and applications team in the Americas region supporting Continental Automotive. Phil also served as new business development manager for the Americas at BAE Systems/Fairchild Imaging prior to joining IHS Markit. Phil holds both a Bachelor of Science in Electrical Engineering and Master of Science in Business degrees from the University of Wisconsin-Madison, US.


Materials and Applications of CMOS Solid-State LIDAR
Louay Eldada, PhD
CEO and Co-Founder
Quanergy

Manufactured at the company’s Silicon Valley factory, Quanergy’s CMOS silicon solid-state LIDAR sensors continue to push the state of the art in capability (high accuracy/high resolution in 3D), reliability, and affordability. With software embedded into the sensor, our LIDAR is the first all-in-one sensing solution. In this presentation, we will describe how CMOS silicon solid-state LIDAR sensors have evolved and matured, and the compelling capability that this type of LiDAR can bring to the industry. We will also discuss Quanergy’s solid-state LIDAR’s past and future developmental milestones such as technology improvements and decreasing costs. We will describe how CMOS silicon solid-state LIDARs have matured, and the compelling automation capability that this type LIDAR, combined with AI software, is bringing to various industries including transportation, smart spaces, industrial automation, drones, robotics, and 3D mapping. Finally, we will present our thoughts about the competitive landscape in the automotive LIDAR segment and discuss the recent activities of the leading players, as well as provide comments on the recent approaches of OEMs and Tier1s with respect to LIDAR integration into the automotive sensor suite.

Biography: Dr. Louay Eldada is the CEO and Co-Founder of Quanergy, the leading creator of high performing, reliable, low-cost LIDAR sensors. He has BS and MS degrees in Electrical Engineering and a PhD in Optical Engineering, all from Columbia University. He studied business administration at Harvard, MIT, and Stanford. Dr. Eldada is a serial entrepreneur, having founded and sold three businesses to Fortune 100 companies, and a technical business leader with a proven track record at both small and large companies. With 71 patents and over 200 technical publications, he is a recognized expert in quantum optics, nanotechnology, photonic integrated circuits, advanced optoelectronics, sensors, and robotics. Prior to Quanergy, Dr. Eldada served as CSO of SunEdison, CTO of HelioVolt (acquired by SK Energy), founding CTO of DuPont Photonics formed by the acquisition of Telephotonics, and CTO of Honeywell’s Telecom Photonics business that he founded and sold to Corning. Louay has received many awards in his field throughout his career, including IEEE Outstanding Speaker Awards, R&D 100 Awards, and an Innovation Award from the Austin Chamber of Commerce.


The Great Wavelength Debate: Which Will Prevail for Automotive LIDAR?
Bahman Hadji
Director of Business Development
ON Semiconductor, SensL Division

With one notable exception, there is no longer a debate among engineers working on the technology to enable autonomous vehicles regarding the necessity of LIDAR as part of the sensor suite to outfit the cars of the future. The debate that remains now is among the LIDAR community itself which is made up of a fragmented ecosystem of suppliers, and is focused on which wavelength of light should be emitted in order to detect the surroundings, given the long-distance ranging and low-cost requirements. There are several main components in a complete LIDAR system and this talk will provide an overview of the market ecosystem with respect to each supplier’s technology offering and preferred wavelength of operation, while also highlighting how further integration can enable higher-performance and lower-cost systems. To explain why two different wavelengths have emerged as the prominent contenders, we must look at the implications that go beyond system cost and ranging performance in the various weather conditions. We will also discuss the types of materials that can emit and detect these wavelengths, their manufacturability, and their availability due to factors such as export control regulations. Furthermore, moving beyond system cost and performance, eye safety will also need to be considered. This talk will outline the above factors in a comprehensive manner and attempt to settle the debate by weighing the pros and cons of each wavelength, as the LIDAR ecosystem inevitably consolidates and converges towards a main wavelength of choice.

Biography: Bahman Hadji has over ten years of experience working with sensing devices in academia and industry for medical, consumer, and automotive applications. In his current role with the SensL Division of ON Semiconductor’s Intelligent Sensing Group, he is focused on bringing to market high-performance sensors used in LIDAR systems and enabling the LIDAR technology ecosystem to leverage ON’s product portfolio. Prior to joining ON Semiconductor in 2017, Bahman held product engineering and product marketing management roles at Aptina Imaging and OmniVision Technologies. Bahman obtained both his Bachelor of Applied Science in Computer Engineering and Master of Applied Science in Electrical and Computer Engineering degrees from the University of Waterloo in Canada.


Automotive LIDAR: Where Do We Go from Here?
Ron Kapusta
Fellow and LIDAR System Architect
Analog Devices

Where do automotive LIDAR systems go next? The first generation of LIDAR products, mostly spinners, have been available for some time now. These were important for technological growth as companies learned the pain points associated with such a complex system. As the market continues to evolve and current players look for customers and partners, they are also looking to make next generation systems. LIDAR signal processors will be the next set of technologies needed for the newest generation of LIDAR systems. Additionally, along with a consolidation of components in the signal chain, competitive size, weight, and power demands are becoming standard requirements for new designs. At the core of these designs are analog to digital converters (ADCs), as well as other components that can be combined to make a more efficient LIDAR sensor. Also, software optimizations can be implemented to create application-specific LIDAR signal processors. This talk will explore how to achieve these enhancements using consolidation concepts and what this means for future LIDAR innovations.

Biography: Ron Kapusta is an Analog Devices Fellow, and also serves as a LIDAR System Architect within the Autonomous Transportation and Safety business unit. Ron holds BS and MEng degrees from the Massachusetts Institute of Technology (MIT). Upon graduation in 2002, he joined Analog Devices, designing data converters and sensor interface circuits for digital imaging systems. In 2014, Ron shifted focus to automotive technologies, where he now works on electronics, photonics, and signal processing for LIDAR. Ron has presented papers and invited educational talks at multiple conferences. He holds more than 30 U.S. patents and has served on the technical program committees for several IEEE conferences.


Solid-State LIDAR: Why It Isn't Necessary for Autonomous Vehicles
Raffi Mardirosian
Head of Corporate Development
Ouster

Solid-state LIDAR has gained a reputation as a necessary component for autonomous vehicles, as compared to spinning and mechanical LIDAR. Solid-state is regarded as the more cost-effective, more robust, and automotive-grade alternative, ideal for mass production of consumer vehicles. However, this perception is based on comparisons against older mechanically spinning LIDAR technologies. Recently, companies have been innovating mechanically spinning LIDAR to achieve smaller form factor, lower cost, higher resolution, greater robustness, and mass production levels, while solid-state LIDAR continues to suffer from challenges ranging from production to resolution to reliability. In addition, most LIDAR sensors marketed as solid-state are in fact based on MEMS technologies, meaning there is still a moving micro-mirror component. This talk will discuss the factors and tradeoffs to consider when choosing LIDAR solutions, rather than the "solid-state" label, and why solid-state technology is not a near term solution for autonomous capabilities. The talk will also provide an overview of the leading companies providing each type of LIDAR, as well as a brief roadmap of technology development for mechanically spinning LIDAR in the near future.

Biography: Raffi Mardirosian is Head of Corporate Development at Ouster. Previously, he was VP, Corporate Development at MODO Fuels, pioneering the production of ultra-low carbon fuels, Principal at Flagship Ventures, where he focused on launching and investing in companies in sustainability and climate change, and President at Midori, a technology startup pioneering its breakthrough carbohydrate catalysis platform with impact across human nutrition, animal nutrition, and human therapeutics. Prior to Midori, Raffi developed renewable power projects in Africa, including creating the first utility-scale solar photovoltaic power plant in East Africa which currently contributes to over 6% of Rwanda's power and broke the record for fastest African energy project finance deal to reach financial close and interconnect.


Silicon Photonics FMCW LIDAR Technologies for Automotive Applications
Ralf Muenster
Vice President, Business Development & Marketing
SiLC Technologies

Integration is key to reducing the cost and footprint of LIDAR and enabling its widespread deployment in many market segments, including automotive. This talk will discuss the application of silicon photonics technology to LIDAR using the frequency modulated continuous wave (FMCW) approach, beginning with an overview of current FMCW LIDAR solution providers and a comparison of FMCW LIDAR to other LIDAR types. Silicon photonics is a proven platform with mature components originally developed for telecom/datacom applications (lasers, detectors, optical couplers, and others), that are now being directly applied to the integration of the photonic functions required for coherent LIDAR. Newer components such as optical phased arrays (OPAs) are under development that will further enhance this functionality and replace current bulky and unreliable mechanical systems. The flexibility of silicon photonics enables a modular approach where the transmission and detection operations, as well as the scanning optics can be separated if required. Thus, performance and cost can be optimized for each application with this modular approach. This talk will include results from recent tests of silicon photonic based FMCW LIDAR systems and discuss potential future developments, together with an assessment of the limitations and challenges associated with the FMCW approach. The talk will also include discussion on the current challenges with the silicon photonics approach, and specifically how these apply to automotive LIDAR applications.

Biography: Ralf Muenster is SiLC’sVice President of Business Development and marketing. He has over two decades of experience commercializing and growing differentiated high-tech businesses and expanding customer and partnership engagements. Prior to SiLC, Ralf was the director of Texas Instruments' CTO office where he was responsible for identifying and developing impactful new growth vectors and strategic technology partnerships for the company. Ralf has held various executive roles in the semiconductor industry, including serving as an intrapreneur and business executive at National Semiconductor, Micrel and AMD, where he was head of the automotive market segment. He founded a successful computer start-up company in Germany and was a scientist at the University of California at Berkeley performing research in the photonics field. Ralf holds a Master’s degree in physics from the Technical University in Munich. Ralf has authored a long list of publications and is frequently invited to speak at leading industry forums. He is also a multiple U.S. patent holder.


An Industry Perspective on LIDAR Algorithms for ADAS Applications
Vikram Narayan, PhD
Head of AI and Computer Vision
Visteon

LIDAR sensors have evolved from being simple time-of-flight devices to truly intelligent sensors which can infer objects and categories. Given these developments, there is an avalanche of research articles which range from applying model-based to deep-learning approaches for solving the objection classification problem on LIDAR point cloud. However, many practical problems which we encounter in developing such systems are understated in the academic world. In this talk, we would like to provide an industry perspective of LIDAR algorithm development, the current problems we are facing, and a few possible solutions for these challenges. We will elaborate on some of the algorithms implemented using LIDARs as an input: particularly lane detection and moving object detection and tracking. We will also discuss several case studies where LIDARs are used not just for sensing, but also for ground truth generation. The talk will conclude with examples where LIDARs actually fail due to environmental conditions and we will propose some measures to address these challenges. Additionally, we will provide a summary of LIDAR software solution providers currently operating in the marketplace, including recently funded startups.

Biography: Dr. Vikram Narayan is currently heading the AI and Computer Vision Team at the ADAS Group of Visteon Corporation based at its corporate headquarters in Detroit, Michgan. He is responsible for camera and LIDAR based sensing technologies and has delivered features such as lane-keeping, which has been tested on the DriveCore ECU, Visteon’s centralized domain controller. In addition, Visteon’s AI and Computer Vision Team has filed several patent applications, and published at premier venues such as ECCV and IVS. Vikram holds a PhD in Robotics from Bielefeld University, Germany and pursued his postdoctoral research at Frankfurt Institute of Advanced Studies, Germany. Prior to his current stint at Visteon, Vikram worked at Panasonic Automotive EU for camera based automatic parking.


Solid-State LIDAR: Principles of Operation and Design Challenges
Slawomir Piatek, PhD
Scientific Consultant
Hamamatsu Photonics

An autonomous vehicle, an engineering goal of the automotive industry, is likely to be equipped with a LIDAR system to provide high-resolution 3D images at distances of up to 200 meters at a video rate. Despite years of active research, designing a working and reliable LIDAR has proven to be very difficult. Two main challenges have been identified: beam steering and photodetection. Most of the current designs rely on mechanical beam steering, which incorporates moving parts. In a harsh driving environment, moving parts are deemed high risk by automotive makers due to reliability concerns. A solid-state LIDAR with no moving parts is an alternative approach, especially with recent advances in the development of distance-imaging SPAD (single-photon avalanche photodiode) arrays. This talk will first review the physics principles of two LIDAR concepts: time-of-flight (ToF) and frequency modulation continuous wave (FMCW), one of which is likely to become a default approach in any solid-state LIDAR. From there, the talk will discuss the three main approaches to a solid-state LIDAR: (1) beam steering and light detection with an optical phased array, or OPA; (2) beam steering with a liquid crystal; and (3) flash illumination and 2D photodetection, concentrating on SPAD arrays. The talk will conclude with comments on the strengths and weaknesses of each approach.

Biography: Dr. Slawomir Piatek has been measuring proper motions of nearby galaxies using images obtained with the Hubble Space Telescope as a senior university lecturer of physics at New Jersey Institute of Technology. He has developed a photonics training program for engineers at Hamamatsu Corporation in New Jersey in the role of a scientific consultant. Also at Hamamatsu, he is involved in popularizing a SiPM as a novel photodetector by writing and lecturing about it, and by experimenting with the device. He earned a PhD in Physics at Rutgers, the State University of New Jersey, in 1994.


Implementing LIDAR Technologies in Autonomous Shuttle Applications
Michael Poulin
Vice President, Product Management
LeddarTech

This session will first provide an overview of the various applications of autonomous shuttles by exploring various potential use cases and will review market estimates, and analyze forecasts. We will also take a closer look at the typical composition of a shuttle sensing suite, review the capabilities and limitations of different detection methods and sensors, and explore how the sensors are integrated into the robotic platform used in autonomous shuttles. We will then look more specifically at different types of LIDARs used in autonomous shuttles, namely scanning 2D/3D LIDARs, and solid-state 2D/3D LIDARs. A particular focus will be placed on flash LIDAR technology and its benefits on autonomous shuttles or other vehicle types. We will explore its contribution to three different use cases, with an emphasis on using next-generation cocoon LIDARs for perception functionality: simultaneous localization and mapping (SLAM), collision avoidance, and vulnerable road user (VRU) detection. After this session, attendees will understand how 3D flash LIDARs can play a critical role in today’s urban AV perception systems, and how they drive value to the overall architecture of autonomous vehicles.

Biography: Michael Poulin has been with LeddarTech since 2010 and is responsible for the leadership of the product management division, which also includes applications engineers and system specialists. This team is devoted to delivering a LIDAR platform that enables our customers to develop their own optimized LIDAR solutions that satisfy their mobility application requirements while maximizing value for them and their customers. Over the course of his career, Michael has acquired extensive experience managing teams that have successfully delivered a wide range of LIDAR and other advanced technological products such as implantable neurosensing and neurostimulation medical devices, as well as motorized prosthetics.


Opportunities for LIDAR in the Growing ADAS Market
Rajeev Thakur, PE
Director Automotive Programs
Velodyne Lidar

Forward collisions account for over 76% of crashes in the United States, with an average cost per crash ofapproximately $45,000. With the increasing addition of ADAS functionalities through NCAP and IIHS regulations, these 2016 numbers are expected to decrease. Improvements in features, such as automatic emergency braking, lane keeping assist, blind spot detection, and cross traffic alertwill make vehicles safer. These safety technologies currently rely primarily on radar and camera for detection of free space and objects. When we study the test protocols for these ratings, we notice that their application in real life conditions is limited by the shortfalls of camera and radar. In this presentation, we will discuss some of our findings on the potential market for LIDAR in ADAS, and how we can make the systems more robust and available with the addition of LIDAR to the sensor suite.

Biography: Rajeev Thakur is currently Director Automotive Programs at Velodyne Lidar where he is responsible for building Velodyne’sLIDARbusiness in the automotive market. In this role, he supports OEM customers to select, design in, and launch Velodyne’s wide LIDARportfolio for autonomous vehicles and ADAS functions. Prior to this, Rajeevwas at OSRAM Opto Semiconductors as Regional Marketing Manager for infrared product management and business development in the NAFTA automotive market. His focus was on LIDAR, driver monitoring, night vision, blind spot detection, and other ADAS applications. Rajeevhas been in the Detroit automotive industry since 1990 – working for companies such as Bosch, Johnson Controls, and Chrysler.Rajeevholds a Master’s degree in Manufacturing Engineering from the University of Massachusetts, Amherst and a Bachelor’s degree in Mechanical Engineering from Guindy Engineering College in Chennai, India. He is a licensed Professional Engineer (PE) and holds a number of patents on occupant sensing. Rajeevis also a member of the SAE Active Safety Standards development committee and a reviewer for IEEE Intelligent Transportation Systems Transactions.


How Will LIDAR Technologies and Business Landscape Evolve?
Nilushi Wijeyasinghe, PhD
Technology Analyst
IDTechEx

LIDAR technologies and markets are rapidly evolving, and we are tracking more than 100 automotive LIDAR companies worldwide. Each company claims to offer a unique, next-generation product that is superior to competing technologies. The technology landscape is cluttered with numerous options for every key component in a LIDAR system, such as laser types and scanning mechanisms. The technology choices which automakers make today will have immense consequences for performance, price, and scalability of LIDAR in the future. The present state of the LIDAR market is unsustainable because winning technologies and companies will inevitably emerge, consolidating the technology and business landscapes. In this presentation, we consider four important questions: How will the business landscape evolve in terms of investments, partnerships, and product development status? How will the technology choices available today impact product positioning? What is the technology roadmap for each LIDAR type? How will each market segment evolve in the short-term and long-term?

Biography: Dr. Nilushi Wijeyasinghe is the analyst leading research on lasers and optical sensors in the photonics division at IDTechEx. She has published comprehensive reports on related topics and now works on autonomous vehicle projects. Nilushi completed her PhD in Experimental Solid State Physics at Imperial College London. Her doctoral research focused on developing new semiconductor materials and cost-effective fabrication processes for optoelectronic devices. Achievements of her PhD include the publication of five lead author peer-reviewed articles, four of which were in journals with impact factor 10 or higher. Prior to joining Imperial, Nilushi obtained a research-based MSc in Atomic & Laser Physics from the University of Oxford, where she developed a laser-based temperature mapping system to monitor gas flows and combustion environments. Nilushi has a First Class Honours degree in MSci Physics from University College London (UCL), where she specialised in Laser Physics and Condensed Matter Physics during her integrated MSc.


Technology Showcase Presenters

(listed alphabetically, by company name)

ADAS to Autonomous Vehicles: Extending the Performance of LIDAR Technologies
Mark McCord
CTO
Cepton Technologies

Interference Rejection: Indispensable Feature for Automotive LIDAR
Que Liu
Business Manager
Hesai Technology

Low Latency Manufacturing for LIDAR Technologies
Nicolas Evans
Sr. Process Engineer
Palomar Technologies

Thermoplastic Solutions for High-Performance LIDAR Optics
Aurelie Schoemann, PhD
Business Manager, Mobility
SABIC

Tony Gioutsos
Director
Siemens

LIDAR and the New Assembly Frontier: Challenges and Solutions
Dhiraj Bora
President
Silitronics Solutions

Light Shaping Optics: Applications in Automotive LIDAR Systems
Georg Ockenfuss
Director Worldwide FAE
VIAVI Solutions

LIDAR and Camera Integration: Technology Challenges and Opportunities
Kris De Meester
Vice President Sales and Business Development
XenomatiX


Startup and Innovation Showcase Presenters

(listed alphabetically, by company name)

Spectrum Scan LIDAR: Comparison to Alternative Technologies
Federico Collarte
CEO
Baraja

Critical LIDAR System Tradeoffs to Maximize Vehicle Safety System Performance
Steve Ehrsam
Vice President, Sales and Marketing
Innovusion

LIDAR Performance without LIDAR: Recent Advances in Camera
Leaf Jiang, PhD
CEO
NODAR

Next Generation Flash LIDAR for Automotive Applications
Scott Burroughs
CEO
Sense Photonics

Past Speakers

Workshop Description and Topics

The conference included a comprehensive workshop on automotive LIDAR with the following sessions:

  1. LIDAR fundamentals: how it works and why it’s necessary for ADAS and autonomous vehicles applications.  This session provided a historical perspective, explain why cameras and radars are not sufficient, and explained the differences between flash and scanned LIDAR.

  2. LIDAR scanning methods and optical considerations. This session included discussion on beam aperture, Rayleigh range, and optical aliasing. Discussion included scanning methods such as mechanical scanners, mirrors, optical phased arrays, liquid crystal waveguides, Risley prisms, and solid state electro-optics. This session also included optical systems such as monostatic and bi-static.

  3. Photon detection and interference. Here, discussion included photon detection methods such as direct and coherent detection, interferers such as benign and malicious, noise and how to cope with it, as well as eye safety considerations.

  4. Mini case studies and overview of 25+ notable LIDAR companies such as Aeva, Aeye, Analog Photonics, Baraja, Blackmore, Cepton, Continental (ASC), Fotonic, Hesai, Ibeo/Valeo, Innoviz, Leddartech, Luminar, Lumotive, OryxVision, Ouster, Panasonic, Phantom Intelligence, Pioneer, Princeton Lightwave, Quanergy, Robosense, Strobe, Tetravue, Velodyne, Waymo, and Xenomatics.

This workshop was presented by Harvey Weinberg, Automotive Division Technologist at Analog Devices.  The workshop was developed in collaboration with Microtech Ventures, a global firm focused on M&A advisory services, management consulting, and business development for MEMS, sensors, and microtechnology companies.

Biography:  Harvey Weinberg is the Division Technologist for the Automotive Business Unit at Analog Devices.  Over the past few years he has been working on long-time horizon technology identification as it pertains to automotive.  Lately this has been principally LIDAR.  Prior roles at ADI have been System Application Engineering Manager for the Automotive BU and before that, leader of the Applications Engineering group for MEMS inertial sensors.  He has 8 US patents in technologies varying from ultrasonic airflow measurement, to inertial sensor applications, to LIDAR systems.  He has been at ADI for 19 years. Before ADI, he worked for 12 years as a circuit and systems designer specializing in process control instrumentation. He holds a Bachelor of Electrical Engineering degree from Concordia University in Montreal, Canada.


Many thanks to our speakers from Automotive LIDAR 2018.

Edge, Centralized, and Fluid Real-Time Processing Techniques of LIDAR Data
Raul Bravo
CEO
Dibotics

When using a centralized architecture, the raw data from the LIDAR sensor is sent to the machine learning "brain" (AI), located in a central processing unit, often a high-end computing platform or GPU. In this scenario, the AI software can fuse data at a low level, but has to sift through high volumes of data, which can take up valuable network bandwidth and have significant costs in terms of time and energy consumption. Smart sensors refer to sensor modules with local processing (i.e. edge computing), where the output is typically a high-level description of detected objects. This approach optimizes the network use and can lower overall costs, but the AI cannot obtain rich-enough information, especially for multi-sensor fusion purposes. The presentation will explain these approaches with specific uses cases and will also introduce a third option, where the edge processing is delivering a “fluid” stream of data: low-level enough to be fused with other LIDARs or radars/cameras, but "smart enough" to decrease network and central processing requirements.

Biography: Raul Bravo is CEO and co-founder of Dibotics, and creator of the Augmented LIDAR technology. He is a serial entrepreneur in mobile robotics and a startup coach, with an extensive 15-year background in both bootstrapped and VC-backed startup creation and growth. He is an engineer from UPC (Barcelona, Spain) with an MBA from College des Ingénieurs (Paris, France). He has filed 10 patents and obtained 27 various awards for his engineering and entrepreneur career, among them the MIT Technology Review’s Top 10 Innovators Under 35 recognition.


Less Is More: Simpler LIDARs and Sensor Fusion for Efficient Autonomous Driving
Ambra Caprile, PhD
Sensor Expert
Magneti Marelli

Signals from several sensors, such as LIDARs, cameras, and radars can be combined through sensor fusion techniques to provide a richer set of data and to improve the reliability of estimation of the trajectory of objects in common driving scenarios. Our development of autonomous vehicle proof of concepts is following the idea that using cost-effective sensors with a reduced feature set, if taken singularly, does not invalidate the efficient and correct functioning of the overall system, provided that the features of each sensor are deeply understood and known in terms of needs for a reliable, repeatable and generally valid sensor fusion. State-of-the-art 3D scanning LIDARs, usually installed on autonomous vehicles and prototypes (such as Uber, Waymo, Ford, and others) to observe the surroundings, match point clouds and detect obstacles in the drivable space, have been substituted by smaller (and cheaper) LIDARs with a highly reduced mechanical complexity. Such devices have, in addition to the advantage of being small, robust, and easily integrated, opened the opportunity for a low-impact equipment design. Their simple performance and functioning opens the question -- what accuracy of the LIDAR sensor data allows a comprehensive reconstruction of the dynamic environment surrounding the vehicle? To provide an answer, it is necessary to conduct detailed characterization studies for each quantity returned by the sensor as an output, in order to achieve a deep knowledge of the sensor features and its behavior in non-conventional scenarios. This presentation will explain these types of approaches and also outline the challenges that still need to be addressed.

Biography: Ambra Caprile graduated from Turin University in Physics of Advanced Technologies, and afterwards won a PhD scholarship at the Politecnico of Turin, also in Physics. Her scientific activity was centered around broadband magnetic properties in soft magnetic materials and was carried out within the Magnetic Materials group of the Italian National Metrology Institute (INRIM). During her PhD research, Ambra also worked at the PTB in Braunschweig, Germany, for a collaboration project on magnetic tunnel junctions. This post-doctoral activity dealt with the analysis of spinwaves in time and frequency domains. Ambra worked in the Optics Division of the Quantum Optics group at the INRIM to the develop an innovative imaging setup for the investigation of magnetic Weiss domains. In 2015, Ambra won an ESRMG within the framework of the EU project SpinCal. At the National Physical Laboratory in Teddington, and in collaboration with Cambridge University and Bielefeld University, she carried out a project aimed at the production and characterization of ion-implanted devices. The investigation was focused on transport properties through anomalous Hall Effect and the effects of FIB modifications. Currently, Ambra is employed at the Innovation Technology department of Magneti Marelli, in the Automated Driving Technologies group, where the focus is aimed at the development of the autonomous vehicle. Her role is centered around the coordination of the activity of characterization of several types of sensors (LIDAR, radars, cameras, and ultrasounds) that provide the environmental perception to the central control of the driverless car.


LIDAR Gets Real: FMCW LIDAR vs. Traditional Pulsed LIDAR
Jim Curry
VP of Product
Blackmore Sensors and Analytics

This talk will discuss the differences of frequency modulated continuous wave (FMCW) LIDAR technologies over traditional pulsed LIDAR systems. Pulsed lidar systems can only measure range, not velocity, and are also susceptible to interference. FMCW LIDAR sensors simultaneously measure both the speed and the distance to any object, giving self-driving systems critical information for safe navigation. This talk will demonstrate how FMCW LIDAR eliminates interference, improves long-range performance, and measures both range and velocity — a triple threat to make autonomous driving safer. Challenges of FMCW LIDAR systems will also be discussed, including range-Doppler ambiguity. Because the expected range frequency and Doppler frequency are both in the megahertz regime, it can be very difficult to separate range measurement from velocity measurement, which can result in errors. FMCW LIDAR is also distinctly different from other sensors currently on the market, requiring people to think differently about how LIDAR sensors are applied. In addition, while FMCW will ultimately be the solution for mass-market LIDAR, large-scale production requires new chipsets which are still in development. We will also cover how the combination of high-resolution 3D and velocity data in the same point cloud is enabling development of new perception algorithms for advanced driver assistance systems (ADAS), autonomous driving, smart transportation, and other applications.

Biography: Mr. Jim Curry has 15 years of experience in the fields of computer science, computer engineering, signal processing, computer vision and computer graphics. An expert in computer hardware and software architectures, Jim has spent his career using desktop, embedded and GPGPU computing as well as FPGAs to acquire, process and visualize data in real-time. Before co-founding Blackmore, Jim lead software development and FPGA design activities at Bridger Photonics, Inc. and was a Principal Member of the Technical Staff at Sandia National Laboratories. Jim has a Bachelor’s of Science in Computer Systems Engineering/Computer Science from Rensselaer Polytechnic Institute and a Master’s of Science in Computer Science from Stanford University.


Evolving ADAS and Autonomy Landscape: Impacts on LIDAR Architecture
Jean-Yves Deschênes
President and Co-Founder
Phantom Intelligence

LIDAR is a relatively late entrant in the automotive sensor set, complementing the video and radar sensor suite. As with any newer technology, expectations have been high, and practical solutions have been taking time to reach the market, as solutions mature. In its early years, LIDAR has not escaped the hype surrounding autonomous car development solutions. Now that the autonomous vehicle development has passed the "peak of inflated expectations", the intelligent car (Level 4-5 and lower level autonomies) community is revising its expectations and adjusting requirements. This creates a strong pressure on LIDAR providers to review the "fit" of their solutions to the new, more pragmatic expectations of the automotive industry. It also creates opportunity for alternative and creative architectures to emerge. This presentation will highlight LIDAR architectures that have emerged in the recent years and current trends leading to future designs. The talk will also provide an overview of existing challenges with LIDAR systems for automotive applications.

Biography: As Co-founder and President of Phantom Intelligence, a Tier Two company that develops core signal processing technology for use in LIDAR-based sensors, Jean-Yves Deschênes has been steering strategic orientations of technologies aimed at the automotive industry for the last seven years. Mr. Deschênes has a software engineering degree from Université Laval in Canada and over 30 years of combined software, optics, and now LIDAR technology experience. Over those years, he has worked on many projects involving international collaboration and completely new uses of emerging technologies, projects that have set standards in their respective industries.


Advanced Physics Based LIDAR Simulation Modeling
Tony Gioutsos
Director of Sales and Marketing
TASS International, A Siemens Business

In order to provide a “due care” testing approach for highly automated vehicles, an advanced sensor simulation must be involved. Although real-world or field tests are required as well as test track testing, simulation can provide a bulk of the testing and also provide tests not producible via real or test track testing. However, to provide the most accurate and best validation, sensor simulation closest to “raw data” would be preferred. In this talk, we will focus on detailed advanced physics-based LIDAR modeling. This type of modeling can be used to produce ROC (receiver operating characteristic) curves, as well as other measures of detection and estimation system performance. Using these measures allows for robust systems for real world operation. Simulations can also be used for the testing and training of AI algorithms. This talk will also include a discussion on the most comment simulation challenges and emerging techniques.

Biography: Mr. Tony Gioutsos has been involved with automotive safety systems since 1990. As Director of Electronics R&D for both Takata and Breed Technologies, he was at the forefront of the safety revolution. His cutting-edge work on passive safety algorithm design and testing led him to start the first automotive algorithm company in 1992. After receiving both his BSEE and MSEE (specializing in Communications and Signal Processing) from the University of Michigan, Mr. Gioutsos worked on satellites and radar imaging for Defense applications before joining Takata. He has been a consultant for various companies in areas such as biomedical applications, gaming software, legal expert advisory, and numerous automotive systems. Mr. Gioutsos is currently Director of Sales and Marketing in the Americas for Siemens PLM where he has continued to define active safety algorithm testing requirements, as well as working on various other state-of-the-art approaches to enhance automated and connected car robustness. He has been awarded over 20 patents and presented over 75 technical papers.


The Role of LIDAR in Multi-Modal, High-Reliability Sensing
Edwin Olson, PhD
Co-Founder and CEO
May Mobility

Developing autonomous vehicles that can operate at very high reliabilities (e.g., one failure per billion miles) requires sensor performance that cannot generally be obtained from a single sensor. In this talk, we describe approaches to developing integrated sensing systems by combining LIDAR with other sensor modalities (e.g. radar), and the usefulness of using multiple types of LIDAR sensors within a single system. Fundamentally, the sensing system must be able to detect, identify, and track objects at long ranges. Ultimately, better systems allow better predictions of a target’s future behavior, enabling a self-driving vehicle to produce better plans. We will describe some of the strengths and weaknesses of currently-available sensor systems and show how careful system design can achieve high performance in real-world conditions at price points that allow autonomous vehicles to be not only safe but also economically viable.

Biography: Edwin Olson is an Associate Professor of Computer Science and Electrical Engineering at the University of Michigan, and co-founder/CEO of May Mobility, Inc., which develops self-driving shuttles. He earned his PhD from MIT in 2008 for work in robot mapping. He has worked on autonomous vehicles for over a decade, including work on the 2007 DARPA Urban Challenge, vehicles for Ford and Toyota Research Institute, and now May Mobility. His academic research includes work on perception, planning, and mapping. He was awarded a DARPA Young Faculty Award, named one of Popular Science's "Brilliant 10", and was winner of the 2010 MAGIC robotics competition. He is perhaps best known for his work on AprilTags, SLAM using MaxMixtures and SGD, and Multi-Policy Decision Making.


An Overview of Photodetectors for Automotive LIDAR Applications
Slawomir Piatek, PhD
Scientific Consultant
Hamamatsu

Whether for a car safety feature or a fully autonomous vehicle, high-resolution information about the distance to other vehicles on the road, unexpected road obstacles, or permanent structures near the road is of paramount importance. It has been realized that LIDAR, due to its superior spatial resolution over radar, is indispensable to providing such information. Surprisingly, despite years of research and development, there isn’t yet consensus which LIDAR concept will be adopted by the automotive market as there are engineering challenges associated with each type. One of these challenges is photodetection. After briefly reviewing the major LIDAR designs – flash, mechanical, MEMS-based, optical phased array, and frequency modulated – this presentation discusses technical aspects behind the selection of photonics components embedded in these systems. Particular attention will be paid to photodetectors such as silicon photomultiplier (SiPM), photodiode (PD), avalanche photodiode (APD), and single-photon avalanche diode (SPAD) and their suitability for various LIDAR concepts.

Biography: Dr. Slawomir Piatek has been measuring proper motions of nearby galaxies using images obtained with the Hubble Space Telescope as a senior university lecturer of physics at New Jersey Institute of Technology. He has developed a photonics training program for engineers at Hamamatsu Corporation in New Jersey in the role of a science consultant. Also at Hamamatsu, he is involved in popularizing a SiPM as a novel photodetector by writing and lecturing about it, and by experimenting with the device. He earned a PhD in Physics at Rutgers, the State University of New Jersey.


An Overview and Comparison of LIDAR Receiver Solutions
Marc Schillgalies, PhD
Vice President of Development
First Sensor

A key component to ensure automotive LIDAR reliability and functionality is the light receiver/detector element. With the significant increase of LIDAR developments, various detector technologies are now used. Laser wavelengths vary from 850nm to 1550nm and require different types of detectors, each with their specific advantages and disadvantages. Different signal amplification concepts also lead to distinct detector technologies. In terms of specifications, signal-to-noise figures are not the only parameters to be considered. Cost, reliability, as well as system capability and availability need to be factored in too. This talk will provided a comprehensive comparison of silicon photodiodes, silicon avalanche photodiodes, silicon SiPMs/SPADs, and InGaAs avalanche photodiodes with respect to SNR, performance under different ambient conditions, bandwidth, differences in signal path architecture, and cost. Furthermore, multi-channel and single-channel detector designs will be compared. The talk will also discuss the next steps in detector development for automotive LIDAR scanners, including robust designs of semiconductor and packaging for harsher environmental conditions and higher integration density.

Biography: Dr. Marc Schillgalies is currently Vice President of Development at the German detector company First Sensor in Berlin. At First Sensor he has worked in different development and product management roles since 2010. Prior to First Sensor he was developing semiconductor laser diodes at Osram Opto Semiconductors in Regensburg, Germany. He received a Doctorate degree and M. Sc. degree in physics from the University of Leipzig, Germany and a M. Sc. degree in Optical Sciences from the University of Arizona in Tucson. In Tucson and Leipzig, his academic work focused on the field of optical semiconductor devices. F urthermore, he received a M.B.A. degree in General Management from Steinbeis University in Berlin with attention to innovation management.


Lasers and Detectors: Requirements, Considerations, and Emerging Trends for Automotive LIDAR
Rajeev Thakur, P.E
Product Marketing Manager, Infrared Business Unit
OSRAM Opto Semiconductors

Being a leading provider of lasers for LIDAR, we are often besieged with requests for new laser designs. This presentation will share some of our insights on the market, the challenges faced in evaluating various LIDAR concepts, and the resulting requirements for lasers. We will discuss the range, resolution, field of view, eye safety, beam qualities, and collimation along with laser packages, laser drivers, detector considerations, and various related topics. Also, we will share a weighted quality function deployment matrix of application requirements and LIDAR design properties along with a few selected LIDAR architectures. We will also discuss sensor fusion gaps, as well as data throughput and regulations. The presentation will also include an overview of some of the interesting and recently funded LIDAR startups, as well as notable technologies that are coming out of universities and R&D labs. Finally, we will present a “crystal ball” outlook for LIDAR technology growth for ADAS and self-driving cars.

Biography: Rajeev Thakur is currently a Product Marketing Manager at OSRAM Opto Semiconductors, where he is responsible for infrared product management and business development in the NAFTA automotive market. His current focus is on LIDAR, driver monitoring, night vision, blind spot detection, and other ADAS applications. Rajeev joined OSRAM Opto Semiconductor in 2014. His experience in the Detroit automotive industry spans over 28 years -- working for companies such as Bosch, Johnson Controls, and Chrysler. He has concept-to-launch experience in occupant sensing, seating, and power train sensors. He holds a Master’s Degree in Manufacturing Engineering from the University of Massachusetts, Amherst and a Bachelor’s Degree in Mechanical Engineering from Guindy Engineering College in Chennai, India. He is a licensed professional engineer and holds a number of patents on occupant sensing. He is also a member of the SAE Active Safety Standards development committee.


2018 Technology Showcase speakers – listed alphabetically, by company name.

John Gach
Business Development – Automotive Interior
OSRAM Opto Semiconductors

Aidan Browne
Applications Engineer
ON Semiconductor

Jane Zhang
CEO
Surestar Technology

Brian Wong
CEO
TriLumina

Georg Ockenfuss
Director WW FAE
VIAVI Solutions


2018 Startup Showcase speakers – listed alphabetically, by company name.

Mohammad Musa
CEO and Co-Founder
Deepen

Kyle Bertin
Business Development
Deepscale

Gleb Akselrod, PhD
CTO and Founder
Lumotive

Joe LaChapelle
Vice President of Research and Development
Luminar

Andrew Miner, PhD
CEO and Founder
Mirada Technologies


Call for Speakers

If you’d like to participate as a speaker, please call Jessica Ingram at 360-929-0114 or send a brief email with your proposed presentation topic to jessica@memsjournal.com. All speakers will receive a complimentary pass to the conference.

Workshop and conference scope includes topics related to LIDAR in automotive applications, such as:

  • Expert reviews and analyses of state-of-the-art LIDAR technologies
  • Business trends, market projections, M&A developments, and startup activity
  • LIDAR data fusion with other types of sensors such as radar and camera
  • Impacts of enabling technologies such as artificial intelligence
  • Notable academic research related to LIDAR for automotive applications
  • LIDAR data processing techniques and algorithms
  • Supply chain trends and challenges, government regulations, and mandates
  • Fabrication, packaging, and system assembly techniques
  • Reliability testing methodologies and techniques
  • Technology transfer, ecosystems and research hubs, company formation