Computational Imaging for Improving Vehicle Safety

This project investigates the design and deployment of sensors and associated algorithms for handling harsh imaging conditions. The research team is particularly interested in depth perception in rain, snow and fog. By expanding the depth range at which objects can be reliably detected, especially in dense fog, the project will facilitate a higher level of safety for vulnerable road users. The project aims to improve perception in both autonomous as well as assisted settings; in the latter, the team will augment perception of human drivers to better identify vulnerable road users in dense fog/rain. Rain, snow and fog present challenging operating scenarios for camera and LIDAR-based depth perception. For passive cameras, imagery in such weather results in a loss of contrast. In turn, this makes it harder to match features across views, which reduces the effectiveness of stereo-based depth estimation. LIDAR, on the other hand, works on the principle of time of flight, measured by pulsing a laser and using a single-photon avalanche diode (SPAD) to measure the arrival time of the first returning photon. Here, fog and rain generate spurious photon arrivals that severely compromise the quality of depth measurements. The team approaches this problem as one of joint design of sensors and algorithms to overcome the challenges imposed by the physics of image formation. The main insight is that improved depth perception can be improved via careful imaging and algorithmic design that allows blocking of photons from the medium while preserving those from the scene of interest. This approach is central to very successful microscopy techniques such as confocal imaging, and diffuse optical tomography, for imaging in highly scattering media like biological tissue. The team will leverage this core intuition but expand it to macroscopic imaging in the real world. Improving perception in dense scattering media by blocking undesired photons requires imaging systems that can selectively choose between favorable light paths in the scene against unfavorable ones. The proposition is to build a structured light system with a high-speed projector and a ultra-high speed SPAD array. With this setup, the team will design patterns that will avoid single-bounce light paths off the medium. An example of this can be seen in prior work by the principal investigator (PI) where a laser line is used to illuminate a scene while sensing it with a line. This imaging configuration ensures light at the intersection of laser and the sensor planes (which is a line in the world) is preferred over other light paths induced by scattering. The team will leverage work by the PI in high-speed depth imaging using SPAD devices; the team will expand upon such a concept for the case of scattering media. This project will enhance the range of scenarios where a vehicle can safely operate in. Specifically, it will lead to increased range in depth perception in fog and rain, improving safety of vulnerable road users. In subsequent years, the team will look at imaging around visual occlusions (like cars) using non-light-of-sight techniques. This will lead to broader adoption of computational imaging with the eventual goal of increasing safety in transportation systems.

• Record URL:
  https://ppms.cit.cmu.edu/projects/detail/439

Language

• English

Project

• Status: Active
• Funding: $255951
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Center Program
  ,    
• Managing Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Carnegie Mellon University

  ,    
• Principal Investigators:

  Sankaranarayanan, Aswin

• Start Date: 20230701
• Expected Completion Date: 20240630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Algorithms; Depth perception; Driver support systems; Fog; Image analysis; Pedestrian safety; Sensors; Vehicle safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01900242
• Record Type: Research project
• Source Agency: Safety21
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Nov 20 2023 8:42PM