Major Recommendations

The following summarizes recommendations from Synthesis’ FY17 work.  These recommendations focus on autonomous vehicles, connected vehicles (vehicle-to-anywhere (V2X)), and LiDAR sensors.  They provide the “consensus” viewpoints, based on Synthesis’ ranking of information collected from 100s of primary sources and 1,000s of secondary sources evaluated during FY17.

1. There is a clear opportunity and need to address the challenge of integrating software and hardware for future mobility applications, particularly mobility use-case-driven R&D gaps in software-enabled, V2X systems are expected to grow significantly.
   
   
2. A taxonomy or roadmap of R&D gaps in autonomous and connected vehicles is needed to depict the attributes and categories of technical gaps.  In this regard, there is an opportunity to lead R&D data collectors to collect, categorize, and quantify the priority or relevance of specific types of R&D gaps– especially in the fast-changing and growing LiDAR, sensors, autonomous and connected vehicle research spaces.
   
   
3. Support for engagement in hardware-oriented sensor R&D, including in:
   a. Substrate-level (e.g., increase wafer size) R&D
   b. Embedded electro-optics design and engineering (e.g. improve imaging resolution, resilience, accuracy)
   c. Glass materials development (e.g., address reduction of REs, and add improved optics and machining properties)
   d. Application-specific electro-optical engineering (e.g. learning-by-doing manufacturing and electrical engineering for human transport applications, in terms of cost, quality and performance)
   
   
4. Two gaps in the V2X and Intelligence Networking category are consensus gaps and deserve VTO’s immediate attention:
   a. R&D on Low-Cost Geo-Localization with 2-3 cm accuracy: Opportunity for design and engineering of low-cost sensors and sensor fusion systems that enable required accuracy for reliable autonomous vehicle applications.
   b. V2X Software-Hardware Integration: Opportunity for guidance, independent test, validation and understanding for software-hardware integration for highly specific AV use cases.
   
   
5. The following top five consensus gaps in the Other R&D Collaboration gap category are recommended for VTO’s immediate attention:
   
   a. Fundamental Competition at Core Technology Level (e.g., Robotics, Machine Vision, AV Sensor Requirements):
   
   The need is for more clarity, commitment, and investment regarding the core figures of merit, the baseline current state and the targets for future R&D – all to accelerate potential breakthroughs in these fields.
   
   b. Scalability of Autonomous Vehicle Engineering (Hardware, Software, Multi-Context, Global Implementations)
   
   The need is to investigate AV engineering approaches across a spectrum of operational contexts (e.g., city, urban, people-transport, things-transport, logistics, on-ground, marine, air, etc.) and to address where scalable solutions are being executed, are feasible, and to identify and share best-practices.
   
   c. Situational Awareness: Data Repository on Technology Used (AVs, Smart City and Smart Grid)
   
   The need is for a verifiable, publicly accessible data source that provides multiple stakeholders (e.g. academics, researchers, investors, inventors and state-local-federal partners) clarity about who is doing what where, in order to catalyze facts-based decision-making in this important and growing field.
   
   d. Systematic Assessment of AV Technology Gaps
   
   The need, as discussed above, is for a systematic, independently derived view on the technology gaps in autonomous and connected vehicle applications. A technology roadmap is recommended based on the high-score for this gap.
   
   e. Need for Opto-Electronic Engineers (including for LiDAR, Optics, Sensor Development Engineering)
   
   The probability that AV technologies, like LiDAR, will be available at significantly lower costs for widespread application depends significantly (indeed, this is a first and foremost driver – based on numerous sources) on the availability of a skilled workforce to ramp up production, reduce costs and maintain quality of end products.
   
   
6. The availability of skilled AV-domain manufacturing engineers is viewed as a key constraint to growth in capacity (esp. if the growth is rapid) of autonomous vehicle technologies (including LiDAR) in the US.
   
   Disruptive Technology Recommendations
   
   The extensive data collection and analysis in FY17 suggests that there are disruptive technology R&D opportunities to consider in the fields of LiDAR-related sensors, connected vehicles, and V2X.  The data also suggests that there may be unique opportunities for US-based job creation by focusing on such leap-ahead innovations.
   
   “Disruptive” is defined as technologies which present opportunities to address significant cost- or cost and performance gap closing needs by one or more orders of magnitude, in which current technology is too costly to scale to address the radical performance increases and cost reductions that are needed. 
   
   Each of the following recommendations regarding disruptive technology reflects Synthesis’ independent assessment of both primary and secondary source research completed under this work-plan.
   
   
7. New I/O control architectures can address multiple on-vehicle intelligence processing functions, at human-transport quality, in fraction of time and fraction of cost.  For example, fusion of all domain control units (DCUs) in one centralized vehicle computer OR new validated, tested architectures that permits “full sense-and-compute” at the edges. New I/O control architectures would present:
   
   a. A solution that removes the requirement to send data off of vehicle, including for map location, navigation or processing.
   b. A solution that is lighter, faster, more secure data generation, collection, mining and processing on vehicle.
   c. solution that can scale in terms of information processing to match the “every car a map-maker and every car a map-user” paradigm.
   
   
8. “Power by Ethernet:” This covers the need to wirelessly charge AVs in many contexts, while mobile or stationary.  An alternative to costly production, install, maintain and package of power through (heavy) motors, batteries and wiring harnesses.
   
   
9. Accelerated engineering of “hard-coded hardware accelerators.” This includes – for example – hard-coded devices that can enable fleet-wide AV software upgrades and that enable such software upgrades to be:
   a. Fail-safe
   b. Valid
   c. Cyber secure

Final Conclusions

The combination of autonomous systems and Internet of Things (IoT) demands new, in-depth understanding of future engineering R&D requirements at every level of the systems engineering process.  Information is (becoming) the new energy.

New partnerships among OEMs, Tier 1-3s, software developers, cyber-security experts, research universities and federal R&D labs are needed to catalyze R&D work in hardware, software and systems-engineering fields.  Such new partnerships are needed to guide the software-based and interdisciplinary work that needs to be done to advance autonomous and connected vehicles.  Synthesis has explored the industry data in this report on a few topics, and has identified several fast-growing fields that will frame the nature of this new R&D reality.

In brief, future R&D will enable systems and components in vehicles to communicate and compute with networks from the component up through the transportation grid, through to a global-level grid.  This is why emerging R&D gaps are numerous, and not only hardware-focused.  From a hardware perspective, autonomous and connected vehicles continuously seek smaller, more functional, more power dense and lower cost designs of every component and sensor.  From an information perspective, rigorous processes for collecting, maintaining and analyzing information about the “information gains” of future R&D is needed.  From a US job and manufacturing growth perspective, this report finds that more skill in engineering-to-manufacturing capabilities is needed.  This simply means more learning-by-doing. 

The fast-emerging, estimated multi-trillion dollar market for autonomous systems is directly connected to (as both a driver and beneficiary of) the Internet of Things.^[1]  Semiconductor manufacturers play a key role in this opportunity.

Autonomous and Connected Vehicles Report (2016-2018)

Introduction

Gap Analysis

Final Recommendations and Conclusions