Autonomous Vehicle Consultant

How will Donald Trump’s election victory impact R&D conducted by the U.S. Government regarding driverless vehicles? There may be reason for a bit of optimism.

During his post-election speech on November 9^th, President-elect Donald Trump stated, “We are going to fix our inner cities and rebuild our highways, bridges, tunnels, airports, schools, hospitals. We’re going to rebuild our infrastructure, which will become, by the way, second to none. And we will put millions of our people to work as we rebuild it.”

In a previous post, I wrote, “Failure to invest in roadway infrastructure in the United States may delay the ultimate commercialization of driverless vehicles. “An estimated 65 percent of U.S. roads are in poor condition, according to the U.S. Department of Transportation, with the transportation infrastructure system rated 12th in the World Economic Forum’s 2014-2015 global competitiveness report.”

“The Huffington Post noted, ‘Shoddy infrastructure has become a roadblock to the development of self-driving cars, vexing engineers and adding time and cost. Poor markings and uneven signage on the 3 million miles of paved roads in the United States are forcing automakers to develop more sophisticated sensors and maps to compensate, industry executives say.’ More advanced sensors will add more cost to driverless vehicles.”

As for federal support of driverless vehicles, “The government’s new support includes a request in President Obama’s proposed budget for the next fiscal year for $4 billion, to be spent over 10 years, to finance research projects and infrastructure improvements tied to driverless cars.” It remains to be seen whether Donald Trump puts this level of spending on a ‘fast track’ to accelerate driverless vehicle development.

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?

The attention-grabbing headlines about driverless vehicles tend to obscure the fact that there is a history to this exciting transportation mode. The trajectory of this history to the present time may portend a path towards the ultimate commercialization of driverless vehicles. 

During 2016

According to Business Insider, “…19 companies are striving to put driverless cars on the road by 2020. These companies include Tesla, Google, Uber, Toyota, BMW, Volvo, Ford, General Motors and Nissan, among others.

During 1995

Much of the current driverless vehicle technology was pioneered by Carnegie Mellon University and culminated with a near driverless trip called, No Hands Across America. “During this tour of America, which was sponsored by Delco Electronics, AssistWare Technology, and Carnegie Mellon University, two researcher from CMU’s Robotics Institute “drove” from Pittsburgh, PA to San Diego, CA using the RALPH computer program.”

“RALPH (Rapidly Adapting Lateral Position Handler) uses video images to determine the location of the road ahead and the appropriate steering direction to keep the vehicle on the road. (The researchers handled the throttle and brake.)”

“RALPH decomposes the problem of steering a vehicle into three steps, 1) sampling of the image, 2) determining the road curvature, and 3) determining the lateral offset of the vehicle relative to the lane center. The output of the later two steps are combined into a steering command, which can be compared with the human driver’s current steering direction as part of a road departure warning system, or sent directly to the steering motor on our Navlab 5 testbed vehicle for autonomous steering control.”

J. H. Everson SBIR Consultant

Politico recently commented on driverless vehicle regulations from the National Highway Traffic Safety Administration (NHTSA). “NHTSA’s guidance for self-driving cars left open the possibility that the agency could seek the power to approve vehicle prototypes before they hit the market…If the federal government had to certify ‘every model, every car,’ it would be a ‘huge barrier to entry and could affect how quickly car models could be introduced,’ said Gary Shapiro, president and CEO of the Consumer Technology Association.”

The Hill recently reported, “NHTSA uses the Federal Aviation Administration (FAA) pre-market regulatory powers as an example of the type of process they would like to emulate…The FAA certification often lasts three to five years…That fact alone should foreclose further discussion about the wisdom of NHTSA employing an FAA-like pre-market approval regime for driverless cars.” As a worst case scenario, nearly 150,000 motorists would have died during a five-year approval review.  Note that there are currently more than 30,000 annual vehicle fatalities in the United States

What’s more important, expanding Government regulations or saving lives?

J. H. Everson SBIR Consultant

Failure to invest in roadway infrastructure in the United States may delay the ultimate commercialization of driverless vehicles. “An estimated 65 percent of U.S. roads are in poor condition, according to the U.S. Department of Transportation, with the transportation infrastructure system rated 12th in the World Economic Forum’s 2014-2015 global competitiveness report.”

The Huffington Post noted, “Shoddy infrastructure has become a roadblock to the development of self-driving cars, vexing engineers and adding time and cost. Poor markings and uneven signage on the 3 million miles of paved roads in the United States are forcing automakers to develop more sophisticated sensors and maps to compensate, industry executives say.” More advanced sensors will add more cost to driverless vehicles.

New street materials could mitigate the need for expensive sensors. “…An easier fix might be customizing road materials to make streets more visible in all kinds of conditions. Roadways can also vary widely in terms of materials and signage. As driverless cars increase in popularity, a new set of road standards will emerge to ensure that street materials and markings are optimized for the new vehicles.”

Repaving 3 million miles of roadways for the benefit of driverless vehicles may slow the commercialization of this form of transportation to a crawl. This effect may delight the auto insurance industry, which could otherwise witness a huge loss of revenue because driverless vehicles are inherently safer than vehicles under human control. Thus, the demand for auto insurance may drastically plummet with the advent of driverless vehicles. This insurance issue was addressed in my previous post.

J. H. Everson SBIR Consultant