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The various levels of autonomous driving systems are still confusing to many individuals, so here's an overview plus a bit of background about where we are with autonomous driving systems and where we might be going.

Autonomous Vehicles

Autonomous Driving Levels 0–5 + Implications

The various levels of autonomous driving systems are still confusing to many individuals, so here’s an overview plus a bit of background about where we are with autonomous driving systems and where we might be going.

Autonomous driving systems are changing the way we think about the future of personal transportation. How soon will we have access to vehicles that don’t require human control? Are driverless cars just around the corner? What will our travel be like if we’re spending a lot less time behind the wheel? What technology actually makes autonomous driving possible? What is autonomous driving, anyway, and what do the different levels entail?

Autonomous vehicles are capable of sensing the surrounding territory and piloting without human input. Experiments surrounding autonomous driving systems began in the 1920s, continued with trials in the 1950s, and reached a critical mass in 2013, when autonomous driving was first formally defined by the National Highway Traffic Safety Administration (NHTSA). The Administration stated that autonomous driving takes place when at least some safety-critical control functions — such as steering, throttle, or braking — occur without direct driver input.

Autonomous Driving Levels 0–5 Explained

Because no two automated driving technologies are exactly alike, in 2014, the Society for Automotive Engineers (SAE) International’s standard J3016 outlined 6 levels of automation for automakers, suppliers, and policymakers to use to classify a system’s sophistication. A crucial shift occurs between Levels 3 and 4, when the driver releases responsibility for monitoring the driving environment to the system.

Level 0 — No Automation: This is the fully human level. It’s the same as when your mother taught you to drive in the parking lot of the local shopping plaza after hours. You accelerate, brake, steer, and negotiate traffic without assistance from any technological device. A car comes at you or a patch of black ice makes the car slide, and you alone must decide how to react safely.

Level 1 — Driver Assistance: You’re still the driver, and you continue to be in charge of mostly every driving function. However, you may call upon technology like adaptive cruise control for support. Adaptive cruise control uses lasers or radar to assess how close your car is to the car in front of you. Then it adjusts the throttle to maintain an appropriate or preset distance. At Level 1, a computer can control either steering or acceleration/braking, but it is not programmed to do both at the same time. At Level 1, you still have full responsibility to monitor road situations and assume all driving functions if the assistance system cannot do so for any reason.

Level 2 — Partial Automation: One assistance system is automated at Level 2. Many luxury automakers are now producing and selling Level 2 cars that can control steering and speed simultaneously, without driver interaction for short periods of time (under 1 minute, and, in some cases, seconds). These cars are the ones that can stay in lanes and hit the brakes for you. The car is able to react to warning systems, can steer, and can change how fast it’s going, but the driver still has to be doing the driving and paying attention to the road.

Level 3 — Conditional Automation: In Level 3 cars, you’re still needed as a driver, but you are able to transfer safety-critical functions to the vehicle, depending on traffic and other conditions. The system manages most of the driving and assesses what’s going on in traffic around you. The system cues you to intervene when it encounters a scenario it can’t navigate, and that’s when you take over. The key point in moving from Level 2 to Level 3 autonomy is that Level 3 expects that the user only has to intervene whenever the car is not able to handle a situation and asks for the user to take over. (Tesla’s Autopilot is considered by many to be at this level. Though, a more critical take is that it’s still at Level 2.)

autonomous driving

Graphic courtesy of SAE International

Level 4 — High Automation: Moving from from Levels 3 to 4 is a significant leap. Level 4 vehicles do it all: they perform all safety-critical driving functions and monitor all roadway conditions for the duration of the trip — while working in operational design domain (ODD). However, you still need to be aware while you’re traveling in the vehicle, as Level 4 does not fulfill every driving scenario. You may have to take over driving controls if certain road types or geographic areas require it. If you own your Level 4 vehicle, you may alternate involvement between active engagement and system management, such as managing all driving duties on surface streets, then becoming a passenger as the car enters a multiple lane highway.

Level 5 — Full Automation: At Level 5, the fully autonomous system is equal to you as the driver in all vehicle functions, traffic, environmental decision-making, and emergency situations. The car can operate on any road and in any conditions you as a human driver could negotiate.

Why is Autonomous Driving Suddenly All the Rage?

Autonomous driving has the potential to invite a massive disruption to what has been generations of unchanged personal transportation mobility. Reasons for the flurry of industry interest in autonomous driving are a combination of three interlocking trends that create the ideal confluence of technology and the marketplace.

◊  Computer vision: Partially, autonomous driving is becoming a reality due to visual object recognition technology, which uses machine vision, including neural networks, to fuse data from multiple sensors and an offline map into current location estimates and map updates. Advances in machine learning have allowed computer vision to finally be good enough to distinguish objects on the road, build 3-D maps of the surrounding area, and be supported by processor speeds powerful enough to be able to operate them in a car.

◊  Ridesharing: Autonomous driving technology continues to be costly, but the introduction of app-based ride-hailing and potentially ridesharing companies like Uber and Lyft has offered other automotive industry insiders with a solution to spread out the capital cost of autonomous driving over multiple drivers. That makes the initial investment seem a lot more feasible and more quickly profitable.

◊  Electrification: While not all autonomous driving features require an EV platform, many automakers are beginning to accept that electric vehicles are a reality and that they are the best choice for fully autonomous vehicles. Whether it is because consumers care about the environment or because EVs are starting to cost less overall to own, automakers have turned the corner on accepting EVs. When industry reps describe adding autonomous driving vehicles to an urban environment, they’re talking about promoting air quality and working alongside governments to meet greenhouse gas emissions reduction goals. That’s a win/win situation for everybody. Also, AVs are targeted for their low operational costs spread over many miles of work in a relatively short period of time, and EVs excel in that arena as well.

What Might the Effects of Fully Autonomous Driving Be — Both Positive and Negative?

Proponents of fully autonomous driving systems envision many positive effects of their mass implementation.

◊  In 2016, motor vehicle-related crashes on US highways claimed 37,461 lives. National Highway Traffic Safety Administration research revealed that 94 % of serious crashes are due to dangerous choices or errors people make behind the wheel. Fewer traffic accidents are often cited as the number one reason for autonomous driving systems.

◊  Commuting becomes a new time for workers to tend to business tasks, thus enhancing productivity.

◊  Automated vehicles replace corporate fleets for deliveries or transporting employees.

◊  The elderly and people with disabilities gain access to the destinations not previously available through public transportation.

◊  Parking lots become fewer and fewer due to ride-hailing services, so urban land is redesignated for public use again.

◊  Energy reduction occurs across all sectors of road transportation.

There are also multiple obstacles to autonomous driving systems, and concerns about their safety continue to hinder widespread adoption. Among the issues are:

◊  Drivers and/or passenger risk loss of privacy.

◊  Many individuals are reluctant to forfeit control of their personal transportation to a driving system.

◊  Concerns about consumer safety are pervasive.

◊  Disputes about liability keep law professors busy: Who is to blame when a crash occurs and autonomous driving systems have been engaged?

◊  Technological challenges like hardware or software failure sometimes force a return to R&D.

◊  Automakers need time to replace their existing stock of vehicles.

◊  Workable legal frameworks about autonomous driving systems are still being promulgated.

◊  US states are inconsistent in their requirements for the automotive industry to report disengagements or failures.

◊  Security concerns, such as hacking or terrorism, arise frequently during conversations about any AI implementation.

◊  The resulting loss of driving-related jobs in the road transport industry is a likely reality and a hard challenge to grapple with.

◊  Increased suburbanization and urban sprawl could alter natural environments as travel becomes less costly and time-consuming.

Terms to Know

In 2014, the SAE International Information Report provided a taxonomy for motor vehicle automation ranging in levels from no automation to full automation. It was a foundation for further standards development activities and offered an initial common language for discussions within the broader “Automated/Autonomous Vehicle” community. Below are some of those terms.

ADS: Autonomous Driving System.

Driving mode is a type of driving scenario with characteristic dynamic driving task requirements (e.g., expressway merging, high-speed cruising, low-speed traffic jam, closed-campus operations, etc.).

Dynamic driving task includes the operational (steering, braking, accelerating, monitoring the vehicle and roadway) and tactical (responding to events, determining when to change lanes, turn, use signals, etc.) aspects of the driving task, but not the strategic (determining destinations and waypoints) aspect of the driving task.

Request to intervene is notification by the automated driving system to a human driver that s/he should promptly begin or resume performance of the dynamic driving task.

radar autonomous truck

The NHTSA’s Role in Autonomous Driving Safety

NHTSA’s newest document about ADSs, released September 2017, is titled, Automated Driving Systems: A Vision for Safety. “Vision” is the keyword in this title, as the document outlines only voluntary guidance — recommendations and suggestions for industry’s consideration and discussion, with absolutely no compliance requirements or enforcement mechanisms.

“The sole purpose of this Guidance is to support the industry as it develops best practices in the design, development, testing, and deployment of automated vehicle technologies…. The Federal Government wants to ensure it does not impede progress with unnecessary or unintended barriers to innovation. Safety remains the number one priority for U.S. DOT and is the specific focus of NHTSA.”

Rather than mandating or legislating autonomous driving safety standards, the automotive industry is “encouraged to follow a robust design and validation process based on a systems-engineering approach with the goal of designing ADSs free of unreasonable safety risks.” And that leaves a lot of design safety that the NHTSA hopes that the industry will consider, including:

“Design architecture, sensors, actuators, communication failure, potential software errors, reliability, potential inadequate control, undesirable control actions, potential collisions with environmental objects and other road users, potential collisions that could be caused by actions of an ADS, leaving the roadway, loss of traction or stability, and violation of traffic laws and deviations from normal (expected) driving practices.”

Any company that manufactures an autonomous driving vehicle would provide the NHTSA with an ODD for each ADS available on their vehicle(s) as tested or deployed for use on public roadways, as well as to document the process and procedure for assessment, testing, and validation of ADS functionality with the prescribed ODD. The company would define each ADS’s capability limits/boundaries for roadway types (interstate, local, etc.) on which the ADS is intended to operate safely; geographic area (city, mountain, desert, etc.); speed range; environmental conditions in which the ADS will operate (weather, daytime/nighttime, etc.);  and other domain constraints.

Object and Event Detection and Response (OEDR) refers to the detection by the driver or ADS of any circumstance that is relevant to the immediate driving task, as well as the implementation of the appropriate driver or system response to such circumstance. An ADS’s OEDR should also include the ability to address a wide variety of foreseeable encounters, including emergency vehicles, temporary work zones, and other unusual conditions (e.g., police manually directing traffic or other first responders or construction workers controlling traffic) that may impact the safe operation of an ADS.

The acceptable minimal performance for an autonomous driving vehicle focuses on properly informing the human operator or occupant through various indicators that the ADS is functioning properly, currently engaged in ADS mode, currently “unavailable” for use, experiencing a malfunction, and/or requesting control transition from the ADS to the operator.

The Voluntary Safety Self-Assessment is intended to demonstrate to the public (particularly states and consumers) that entities are:

“(1) considering the safety aspects of ADSs; (2) communicating and collaborating with DOT; (3) encouraging the self-establishment of industry safety norms for ADSs; and (4) building public trust, acceptance, and confidence through transparent testing and deployment of ADSs. It also allows companies an opportunity to showcase their approach to safety, without needing to reveal proprietary intellectual property… Entities are not required to submit a Voluntary Safety Self-Assessment.”

Do you agree, as the NHTSA document states, that voluntary federal guidance is satisfactory to implement a new era of autonomous driving? Does highlighting the 12 priority safety elements and an associated Voluntary Safety Self-Assessment really offer public reassurance that safety remains NHTSA’s top priority? We’re still caught in an era when most consumers express trepidation about using a Level 4 or 5 autonomous driving vehicle. Companies are taking explicit measures to increase consumer familiarity with ADSs, and celebrities like LeBron James have been enlisted to help assuage those consumer fears.

As autonomous driving Levels 3–5 are reached, new priority safety design elements do need to be considered, such as vehicle cybersecurity, human machine interface, crashworthiness, consumer education and training, and post-crash autonomous driving system behavior. Is the industry up to the task to be self-assessing and regulating in these important safety areas?

I don’t know about you, but, as a lifelong instructor, I’ve found that individuals tend to engage in something difficult and new when they’re required to do it. Maybe the NHTSA should talk to scholars in the field of adult learning theory. Just a thought.

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Written By

Carolyn Fortuna (they, them), Ph.D., is a writer, researcher, and educator with a lifelong dedication to ecojustice. Carolyn has won awards from the Anti-Defamation League, The International Literacy Association, and The Leavy Foundation. Carolyn is a small-time investor in Tesla. Please follow Carolyn on Twitter and Facebook.


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