Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems
Abstract
This Information Report provides a taxonomy for motor vehicle automation ranging in level from no automation to full automation. However, it provides detailed definitions only for the highest three levels of automation provided in the taxonomy (namely, conditional, high and full automation) in the context of motor vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on public roadways. These latter levels of advanced automation refer to cases in which the dynamic driving task is performed entirely by an automated driving system during a given driving mode or trip. Popular, media, and legislative references to “autonomous” or “self-driving” vehicles encompass some or all of these levels of automation. These definitions can be used to describe the automation of (1) on-road vehicles, (2) particular systems within those vehicles, and (3) the operation of those vehicles. “On-road” refers to public roadways that collectively serve users of vehicles of all classes and automation levels (including no automation), as well as motorcyclists, pedal cyclists, and pedestrians. This document does not provide complete definitions applicable to lower levels of automation (namely, no automation, assisted, or partial automation), but they are described as points of reference to help bound the full range of vehicle automation. Active safety and driver assistance systems that partially and/or temporarily automate certain aspects of vehicle operation (including systems that automatically intervene to avoid and/or mitigate an emergency situation and then immediately disengage), but otherwise rely on a human driver to operate the motor vehicle in real time, are also not included within the conditional, high, and full automation taxonomy types that are the focus of this document.
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The main sensing technologies differ in both companies: Tesla tries to leverage on its camera systems, whereas Waymo's driving technology relies more on Lidar sensors.
“The main sensing technologies differ in both companies. Tesla ® tries to leverage on its camera systems, whereas Waymo's driving technology relies more on Lidar sensors 4 .”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [4]
Waymo makes use of three Lidar sensors, five radars and eight cameras, while Tesla cars are equipped with eight cameras, 12 ultrasonic sensors and one forward-facing radar.
“For example, alongside three Lidar sensors, Waymo also makes use of five radars and eight cameras, while Tesla ® cars are equipped with eights cameras, 12 ultrasonic sensors and one forward-facing radar.”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [4]
Tesla and Waymo, two of the companies leading the development of self-driving technology, have different philosophies with respect to their main perception sensor, as well as regarding the targeted SAE level.
“Tesla ® and Waymo ® , two of the companies leading the development of self-driving technology [30], have different philosophies with respect to their main perception sensor, as well as regarding the targeted SAE level [4].”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [4]
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Milakis, D., Snelder, M., Arem, B. V., Wee, B. V., & Homem de Almeida Correia, G. (2017) · European Journal of Transport and Infrastructure Research · doi:10.18757/ejtir.2017.17.1.3180 · Cited by 194 · see citing works
Molnar, L. J., Ryan, L. H., Pradhan, A. K., Eby, D. W., St. Louis, R. M., & Zakrajsek, J. S. (2018) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2018.06.004 · Cited by 172 · see citing works
SupportingTrust in Autonomous Driving
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Liu, P., Du, Y., & Xu, Z. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.02.012 · Cited by 65 · see citing works
Bellet, T., Banet, A., Petiot, M., Richard, B., & Quick, J. (2020) · Information · doi:10.3390/info12010013 · Cited by 12 · see citing works
Suryana, L. E., Nordhoff, S., Calvert, S., Zgonnikov, A., & van Arem, B. (2025) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.04.026 · Cited by 6 · see citing works
- Authors
- On-Road Automated Driving (ORAD) Committee
- Corresponding author
- On-Road Automated Driving (ORAD) Committee
- Publisher
- SAE International
- Published
- 2014-01-16
- OpenAlex
- W4249261707
- Cited by
- 675
- References
- 0
- Citations by year
- 2026: 3 · 2025: 24 · 2024: 63 · 2023: 63 · 2022: 80 · 2021: 87 · 2020: 73 · 2019: 87 · 2018: 76 · 2017: 57 · 2016: 41 · 2015: 18 · 2014: 3
- Type
- standard
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- autonomous driving, levels of driving automation, conditional automation, high automation, full automation, road vehicles, autonomous vehicles, active safety systems, driver assistance systems
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