Tesla Model S Fatal Crash – Was the Optical Sensor at Fault?07 Nov

SBIR CONSULTANT BULLETIN – See National Science Foundation SBIR Proposal Topic, Sensors (SE) for Potential Bidding Opportunities

There was a tragic crash of a Model S Tesla on May 7, 2016 in Williston, Florida. The driver, Joshua D. Brown, died from this accident.

According to Tesla, “ What we know is that the vehicle was on a divided highway with Autopilot engaged when a tractor trailer drove across the highway perpendicular to the Model S”… “Neither Autopilot nor the driver noticed the white side of the tractor trailer against a brightly lit sky, so the brake was not applied.” 

“The current vintage of Model S autopilot cars (Q4 2014 to Q1 2015) have one front radar, one front monocular Mobileye optical camera, and a 360-degree set of ultrasonic sensors.”

To what extent was this optical camera optimized to deal with a multitude of driving scenarios with varying contrasts due to the roadway, sky, cars, trucks, pedestrians and miscellaneous objects?

I addressed this contrast problem in a paper that was published in 1994 (Proc. Of SPIE, Photonics for Electronic Products, Vol. 2544, Boston, MA, 1994). The subject of this paper was contrast optimization of optical systems for Run-Off-Road crash avoidance.

According to my paper, “Contrast is determined by material properties affecting reflected and radiated intensities, as well as weather and visibility conditions. This paper discusses the modeling of these parameters and characterizes the contrast performance effects due to reduced visibility. The analysis process first involves generation of inherent road and off-road contrasts, followed by weather effects as a contrast modification…The results of the sensor/weather modeling will be used to predict the performance off an in-vehicle warning system under various levels of adverse weather.”

“Varying contrast results were generated for…original image (a divided four lane highway) and three sets of corresponding images related to fog, medium rain and fog plus light rain…The degraded images…included atmospheric effects due to transmission loss and the addition of path radiance… These contrast plots were created to underscore the need for an optimum sensor waveband for run-off-road collision avoidance.”

Contrasts can be considered as signal to noise ratios where higher ratios can be correlated with greater detection probabilities for cars, trucks, buses, pedestrians, etc. Such ratios could be included in detection warning algorithms for driverless vehicles. Low ratios could be used to trigger driver-warning algorithms.

  • Optical Camera: To what extent, if any, was the Tesla Model S algorithm developed with detection probabilities for various driving scenarios?
  • Who is responsible for testing optical camera detection algorithms?
  • Who is responsible for assessing other sensor modality algorithms for radar, LIDAR and acoustic?
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About Dr. Everson

Prior to forming this SBIR consultant practice, Dr. Jeffrey Everson was director of business development for QinetiQ North America’s Technology Solutions Group (previously Foster-Miller, Inc.).

Dr. Everson has won and been the principal investigator for several SBIR programs, including a Phase I program for NASA, a Phase I project for the U.S. Air Force, and Phase I and II contracts from the U.S. Department of Transportation. For the Phase II program, he received a Tibbetts Award for exemplifying the best in SBIR achievement.

Previously Dr. Everson held senior scientist positions at Battelle Memorial Institute, The Analytic Sciences Corporation (TASC), Honeywell Electro Optics Systems Division, and Itek Optical Systems Division.

He holds a PhD in physics from Boston College and a MS/BS in physics from Northeastern University.

Contact

For more information about how JHEverson Consulting can help your company with its SBIR and STTR proposals, please contact Jeff Everson.

JHEverson Consulting is based in the Boston area but consults for clients throughout North America. It also is supported by affiliated consultants.