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.” 
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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. 
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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. 
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The automotive industry has long been a global market where manufacturers need to constantly monitor international laws and regulations. But as traditional automotive OEMs expand their product offerings to include services such as ride hailing, car sharing, and mapping, the legal risks are becoming increasingly localized.

Cities, states, and provinces have begun to flex their muscle in response to the introduction of new mobility products and services. New York city has placed a cap on the number of vehicles for ride-hailing platforms and will institute congestion pricing in early 2021. Los Angeles has created a tool for data collection and monitoring of private mobility-as-a-service (MaaS) companies. Peer-to-peer car sharing companies that compete with traditional rent-a-car agencies have challenged laws requiring them to pay local rental fees. And mapping services have been forced to navigate legal concerns over which local streets they can route users through. 
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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. 
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