On November 19, 2020, NHTSA issued an Advance Notice of Proposed Rulemaking (“ANPRM”) asking interested parties and the public to provide the agency with information intended to help NHTSA devise a “Framework for Automated Driving Safety” that will allow the agency to implement safety standards for the operation and performance of automated driving systems. Comments are due 60 days after the ANPRM is published in the Federal Register. NHTSA last addressed AV in March, when it issued a Notice of Proposed Rulemaking addressing occupant safety and crashworthiness for AVs that do not have typical seating configurations. This notice addresses operation of the automated systems in SAE Level 4 and 5 vehicles. 
Continue Reading NHTSA Releases Advance Notice of Proposed Rulemaking For Automated Driving System Safety

The California Consumer Privacy Act (“CCPA”), Cal. Civ. Code 1798.100-199, presents some interesting questions for mobility businesses and service providers that handle data developed or transmitted by vehicles. Although the CCPA was passed with an effective date of January 1, 2020, the regulations implementing it are still in flux—and are on their second iteration. But whether final regulations are in place or not, enforcement by the California Attorney General’s office could start as early as July 1, 2020.  Because the CCPA provided only limited exemptions for information collected by the automotive industry—information collected under the Driver’s Privacy Protection Act of 1994 and certain information developed and exchanged by new auto dealers and vehicle manufacturers in connection with warranty work or vehicle/part recalls—significant questions remain as to how the CCPA will be applied to the mobility industry.

For the past hundred or so years, most vehicles did not have the electronic brains to require a CCPA “gut check.” When electronics made their debut in automobiles, tools like OBD allowed vehicles to store diagnostic codes, and eventually event recorders (now regulated by the Driver Privacy Act of 2015) recorded pre-accident conditions. Telematics began to change the picture in the late 1990s, with automobiles transmitting information to central locations using cellular (and now wireless) technology. Modern connected vehicles can collect vast amounts of data when driven—and they can pass large amounts of it to manufacturers and service providers. And even when they are not actively transmitting this information, such information can be extracted from vehicles by service personnel. SAE Level 4 and Level 5 autonomous vehicles will necessarily be more dependent on connectivity both to central data sources and to each other—and can be expected to drive an explosion in data transmitted and analyzed on a central basis. Some of this will be regulated by data privacy laws, such as the CCPA, despite the above noted exceptions for automotive information.
Continue Reading CCPA: Keeping the Wheels on the Road

Earlier this month, Nuro made automotive history when it became the first company to obtain a NHTSA exemption for a driverless vehicle, the R2. Nuro is a self-driving startup created by two former Google engineers. Their earlier version, the R1, is a small electric ‘van’ without a steering wheel or pedals and  designed exclusively for delivery of goods rather than people.

Nuro has been testing the R1 vehicle on public roads in places like Scottsdale, AZ for several years. This vehicle is technically classified by federal regulations as a low speed vehicle (LSV) meaning it has a maximum speed of 25 mph and maximum weight of 3,000 lbs. As a low-speed vehicle, the R1 did not need to satisfy all of the FMVSS requirements for passenger cars and trucks (i.e. standards relating seatbelts, airbags, and steering). But it must still satisfy the minimum requirements of FMVSS 500 including front lights, rear view mirrors, and windshields. Therefore, since 2018, the Nuro R1 has been equipped with these safety features, despite having no human present in the vehicle. For its new R2 vehicles, Nuro requested three exemptions for their driverless delivery vehicle:
Continue Reading Nuro, NHTSA, and the New Autonomous Vehicle Exemption Rules

On November 25, 2019 the National Transportation Safety Board issued its final report on the March 2018 fatality accident in Tempe, Arizona, involving an autonomous vehicle and a pedestrian. NTSB’s position on the accident is that it came about because of a combination of an “inadequate safety culture” at the developer and “automation complacency,” which it describes as the failure of the human safety driver to “monitor an automation system for its failures.”

It doesn’t take a deep dive into news reports about AVs to see that many developers envision AVs as systems in which vehicle occupants are entirely “automation complacent”—watching movies, playing video games, doing business or otherwise ignoring the outside world. Everybody from BMW to IKEA has presented concepts for AV interiors, involving everything from social media feeds or e-mails on vehicle windows to projections of movies or video games on the windshield. Add in noise-cancellation technology, lighting and temperature control (not to mention alcohol) and the AV is designed to induce “complacency,” exactly what NTSB criticized in this accident as well as the Tesla Autopilot crashes in Florida and Culver City, California: “driver inattention and overreliance on vehicle automation.” As Hamlet reminds us, “there’s the rub.” 
Continue Reading The NTSB and “Automation Complacency”

The good news is that improved safety is far and away the predominant force behind development of advanced driving technologies. This is evident from the NHTSA’s latest Automated Vehicles 3.0, in which the word “safety” appears no less than 318 times. Industry has also prioritized safety as the race to commercialize autonomous driving technology will require, as a threshold matter, broad acceptance from the public in order to realize investment returns. Driverless vehicles promise other benefits too. The simple convenience of reading a book or taking a nap while commuting home from the office sounds wonderful. But until the technology is considered safe enough for our shared roads, such ancillary perks are merely hypothetical. 
Continue Reading Driverless Vehicles Will Be Here as Soon as They Are Safe Enough, but What Does That Actually Mean?

Virginia may not be a state many people associate with autonomous technology, but it has quickly become an attractive locale for developers of this technology. Several autonomous technology companies have recently seen great advancements in the implementation of their technology in Virginia. WING, for example, a sister company of Google, recently celebrated its first successful aviation delivery of commercial products in Christiansburg, Virginia. The company, which has partnered with Walgreens and FedEx, uses drones to deliver packages to the community. Aurora Flight Sciences, an independent subsidiary of Boeing, similarly completed its first test flight of its autonomous passenger air vehicle prototype in Manassas, Virginia earlier this year. In addition, San Francisco based LM Industries deployed a self-driving vehicle, Ollie, at Fort Myer-Henderson Hall in Arlington earlier this year, and Optimus Ride, a Boston-based self-driving tech company, has deployed autonomous shuttles in Reston, where more than 15,000 rides have already been completed. 
Continue Reading Preparing Now for the Road Ahead

Currently, nearly all advanced automotive technologies operate under what are known as deterministic models. In a deterministic system, the outcome is dictated by a set of known initial conditions. These systems offer welcome predictability and repeatability. In contrast, probabilistic models yield different outcomes given the same initial condition, introducing an element of probability guided randomness. The latter model is considered necessary for autonomous vehicle functionality, but this is easier said than done.

Each model type has pros and cons. For starters, in the area of advanced automotive technologies, programming a deterministic model is more manageable. For example, automatic emergency braking systems are generally calibrated to recognize the rear of a preceding vehicle and apply braking when that vehicle slows and a crash is imminent. But how would that same system respond to a boulder rolling onto the roadway in the path of an equipped vehicle? The deterministic model is not well-suited for a curveball like this. The driver wants the same outcome – braking assistance – but the system only recognizes a certain set of predetermined conditions that do not include rolling boulders. 
Continue Reading Our Expectations for Advanced Vehicle Technologies are Outpacing the Technology