Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Dec 28;23(1):317.
doi: 10.3390/s23010317.

How Do Autonomous Vehicles Decide?

Sumbal Malik  1   2 , Manzoor Ahmed Khan  1   2 , Hesham El-Sayed  1   2 , Jalal Khan  1 , Obaid Ullah  1
Affiliations
Review

How Do Autonomous Vehicles Decide?

Sumbal Malik et al. Sensors (Basel). .

Abstract

The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.

Keywords: autonomous driving; behavioural planning; decision-making.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
SAE Levels of Driving Automation.
Figure 2
Figure 2
Software Architecture of Autonomous Vehicle.
Figure 3
Figure 3
Categorization of Decision-Making Approaches for Autonomous Vehicles.
Figure 4
Figure 4
Simple Finite State Machine for Autonomous Vehicle Overtaking Maneuver.
Figure 5
Figure 5
A framework of traffic mirror-aware POMDP behavior planner.
Figure 6
Figure 6
Coalitional game theory based decision-making framework for CAVs.
Figure 7
Figure 7
Four types of coalitions. (A) The single player coalition; (B) The multi-player coalition; (C) The grand coalition; (D) The grand coalition with a sub-coalition.
Figure 8
Figure 8
Illustration of Reinforcement Learning Approach.
See this image and copyright information in PMC

References

    1. Batkovic I. Ph.D. Thesis. Chalmers Tekniska Hogskola; Gothenburg, Sweden: 2022. Enabling Safe Autonomous Driving in Uncertain Environments.
    1. Taxonomy and Definitions for Terms Related to Driving Automation Systems for on-Road Motor Vehicles. SAE International; Warrendale, PA, USA: 2016.
    1. Claussmann L., Revilloud M., Gruyer D., Glaser S. A review of motion planning for highway autonomous driving. IEEE Trans. Intell. Transp. Syst. 2019;21:1826–1848. doi: 10.1109/TITS.2019.2913998. - DOI
    1. Pendleton S.D., Andersen H., Du X., Shen X., Meghjani M., Eng Y.H., Rus D., Ang M.H. Perception, planning, control, and coordination for autonomous vehicles. Machines. 2017;5:6. doi: 10.3390/machines5010006. - DOI
    1. Katrakazas C., Quddus M., Chen W.H., Deka L. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transp. Res. Part C Emerg. Technol. 2015;60:416–442. doi: 10.1016/j.trc.2015.09.011. - DOI

LinkOut - more resources