Autonomous and Connected Vehicles Report: Vehicle Sensors and Intelligence Materials Gaps

Findings from the Vehicle Sensors and Intelligence Materials:

  • All gaps in this category have high work-plan relevance scores; which is to say at least 20 out of a maximum 30. This reflects the relative significance of two or more of the attributes of private-sector investment gap, 5-year time horizons and hardware R&D among topics in this gap category.
  • These gaps indicate in particular numerous pathways to hardware-focused R&D across the full spectrum of the sensor and sensor application-engineering development chain.
  • 30% of the total gaps discovered (11 of 37) relate to vehicle sensors and intelligence materials.
  • 46% (5 of the 11 gaps) of gaps in this category are assessed by Synthesis as consensus gaps, indicating that speakers at the AVS 2017 plenary summary sessions addressed these topics as gaps in their concluding slides and accompanying discussions, per Synthesis analyst attendance at AVS 2017 in San Francisco.  These gaps appear to have the largest number of experts in agreement, based on the non-statistical, non-survey-based, primary source research in this study.
  • The following consensus gaps that would benefit from immediate attention:
    • Sensor Cost Reduction;
    • Scalable Low-Cost Autonomous Vehicle (AV) Black Boxes;
    • Sensors for Complex Driving Environments (e.g., snow, fog, rain, sleet, etc.);
    • Onboard Scalable Low-Cost Power and Processing (to enable sensor operations); and
    • Sensor Fusion.
  • The six remaining gaps in this category – from miniaturized processors to memory supply chain bottleneck – are not considered consensus gaps at this time.  Nonetheless, they deserve careful consideration because, from among hundreds of sources contacted, primary sources took time to recommend the gap to Synthesis in an in-depth interview.

Autonomous and Connected Vehicles Report (2016-2018)

Introduction

Gap Analysis

Final Recommendations and Conclusions

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