Autonomy Glossary: Key Terms to Know

With the autonomous vehicle market projected to reach USD $724,36B by 2027*, these common terminologies, phrases and system names can provide a high-level overview of the industry. 

5G  Soon to become the communications backbone of autonomous vehicles, 5G will be used to connect self-driving cars with each other and the world around them. There will be connections between vehicles, vehicles and the cloud, and between vehicles and infrastructure, such as traffic lights. The enhanced speed of 5G means this technology significantly surpasses 4G capabilities.
Adaptive Using information around a vehicle to adjust its behavior. Often used with "adaptive cruise control," where a car can increase or lower its speed depending on the traffic, as well as the speed of the car directly in front of it. Cars utilizing a forward-facing radar system allow for adaptive response. 
Advanced Driver Assistance Systems (ADAS) Electronic systems in a vehicle designed to aid the driver in improving driving safety; for example, adaptive cruise control and forward collision warning. 
Artificial Intelligence (AI) A widely used phrase these days, but AI is exactly what powers autonomous vehicles. Autonomous vehicles use AI to understand the world around them, predicting how surrounding traffic and pedestrians will move, and anticipating the flow of traffic ahead.
Autonomous A fully autonomous car, or vehicle, is one that can run—and drive—without any human intervention at all. This would mean a vehicle without controls or steering. 
Cloud Connected Devices linked through the cloud, i.e. the Internet. Most wireless devices that speak to each other are cloud connected—sending information wirelessly to the Internet, which then another device reads by connecting wirelessly to the Internet to read. Autonomous and connected cars use the cloud to receive software updates from their manufacturers, but will eventually use a 5G connection with the cloud to learn about the road ahead from other vehicles, and issue alerts to those behind if they encounter a hazard, such as construction or a blocked road.
Computer Vision This is the technology which helps vehicles to 'see', and employs a wide range of systems, including lidar, radar, and visible-light cameras. 
Deep Learning Akin to Artificial Intelligence (AI), deep learning is a subset of machine learning that involves many layers of processing, data and significant compute capacity. These deep learning algorithms can facilitate computer vision, natural language processing, driving strategy, personalization and even decision-making.
Diagnostics port Also called an OBD port (for on-board diagnostics port), this is usually located under the dash, and is in basically every car made after 1996. Mechanics often link into this port to read diagnostic details from your car, and detect problems. Many wireless products for your car are plugged into the ODB or diagnostics port.
Driver Assistance A far cry from self-driving capability, driver assistance tools are those that kick in for a few seconds or minutes—but do not take operations away from the person steering the wheel. Common examples are lane warnings if a car steers out of a lane, and obstacle warnings such as a car getting too close to another vehicle when parallel parking.
Driver Monitoring System Before cars become fully autonomous, they will use their enhanced intelligence to keep an eye on the human driver. Systems can check that the driver is awake by monitoring their inputs to the steering wheel. Beyond this, cars will start to use cameras to check the driver is awake, and sober. 
Hive Mind Multiple people who openly share their knowledge and opinions, which may lead to collective intelligence or uncritical compliance 
Industry 4.0 This overarching term refers to the 4th industrial revolution. Increasing interconnectivity and smart automation is impacting the technology, industries and societal patterns and processes in the 21st century - improving efficiency and causing a significant shift in industrial capitalism. 
Levels of Autonomy Generally speaking, there are five different levels of autonomy, from 0 to 5, where Level 1 would include adaptive cruise control, and a Level 5 would give all controls to the driving system. Here is a quick breakdown of each level: 

• Level 0: No automation. A human controls all driving tasks, even when aided by warning systems.
• Level 1: Driver assistance. A human operator performs most tasks; simple systems provide basic guidance that optimizes human decision-making.
• Level 2: Partial automation. An operator is hands-on with minimal guided assistance. The autonomous vehicle can take over basic functions such as speed, steering and machine components.
• Level 2+: Enhanced partial automation. Operator is hands-off, eyes-on. The vehicle controls critical systems such as precision blade control and in-lane positioning.
• Level 3: Conditional automation. The operator is hands and eyes off, but mind on. The machine operates completely independently in limited, well-defined scenarios with only human observation.
• Level 4: High automation. The operator’s eyes, hands and mind are focused on creating value. Operators are able to automate an end-to-end workflow.
• Level 5: Full automation. The machine operates with full autonomy. The vehicle will fully replace traditional human operator tasks, going anywhere and doing anything an experienced human operator can currently achieve.

Lidar  Acronym for light detection and ranging. Similar to radar, laser-based lidar is used by autonomous vehicles to see the world around them. Designed for spotting nearby objects, lidar systems sit on the roof or are incorporated into the body of a self-driving vehicle, and spin around to gather a detailed view of their surroundings. This is then used by the car's onboard computer to identify vehicles, pedestrians, and other objects on the road that it needs to be aware of, and potentially interact with (or avoid).
Mobility-as-a-Service (MaaS) Fueled by the emergence of ridesharing and carpooling services, this is an emerging type of service that, through a joint digital channel, enables users to plan, book, and pay for multiple types of mobility services. The concept describes a shift away from personally-owned modes of transportation and towards mobility provided as a service. It includes transportation options which extend beyond the car, but fall short of mass public transportation like buses, trains and airplanes. In the ideal world, a MaaS company would offer a flat monthly subscription for access to cars whenever you need them, along with electric scooters, small ride-sharing vehicles, and other kinds of transport, to get wherever you need to go, as efficiently and cheaply as possible.
NHTSA The National Highway Traffic Safety Administration is a United States federal regulatory agency that enforces traffic safety standards, and has created classifications for automated vehicles. The group investigates incidents around self-driving cars.
OTA software update Short for over-the-air, this refers to software updates which are given to vehicles remotely, via their internet connection. Because most new cars contain a SIM card (or several) with a 3G or 4G connection, manufacturers can push software updates to them, in the same manner smartphones get updates, new features, and bug fixes.
Platooning A driving technique where autonomous trucks follow each other on the highway. Initially, this will see the lead truck driven by a human, with the other computer-controlled follow behind. By following each other very closely, the trucks create less aerodynamic drag, lowering the amount of energy needed to move them forward, thus increasing their efficiency and range, while reducing traffic.
Precise Point Positioning (PPP) This positioning method uses 3rd party correction services such as RTX to enhance GNSS positioning for higher levels of accuracy, with errors as small as a few centimeters. Autonomous vehicles utilize PPP to attain lane-level positioning accuracy.
Radar Acronym for radio detection and ranging. Radar is a system that uses radio waves to detect other objects. Self-driving cars can be equipped with radar to help the vehicle read the area around it.
Real-Time Extended (RTX) Based on the generation and delivery of precise satellite corrections, Real-Time Extended (RTX) positioning is a technology produced by combining a variety of innovative techniques, which together provide users with centimeter-level real-time position accuracy anywhere on or near the Earth’s surface. 
Self-Driving Self-driving is the ultimate goal of many car makers today, a vehicle that can tell what's around it and operate completely without any human involvement.
Sensors In self-driving cars, sensors are devices that a vehicle uses to detect what's around it, including obstacles in the road such as pedestrians to other cars. These details can also link to GNSS technology to tell a vehicle its position on the road.
V2V Short for ‘vehicle-to-vehicle’ this is the term used to describe a network which will connect autonomous vehicles to other vehicles on the road.
V2X Short for 'vehicle-to-everything' this is the term used to describe a network which will connect autonomous vehicles to the world around them. This could include infrastructure like traffic lights, parking lots, and sensors monitoring weather, road conditions, pollution levels, and much more.
*Autonomous vehicle market by component, application and fuel type forecasts to 2027. Emergen Research, September 2020.