Autonomous driving refers to automated vehicles that can operate without a human driver. People often call these vehicles driverless cars or self-driving cars. The term autonomous has become synonymous with robotics and products using artificial intelligence (AI). When these are present in a vehicle, AI either supports the human driver or performs the entire driving task.

Many terms are used to describe this field of research. The Society for Automotive Engineers (SAE International) prefers the term ‘vehicles with automated driving systems’. It suggests this term best describes and encompasses the various levels of autonomy. This applies to vehicles with partial automation as well as fully self-driving cars.

This article is an introduction to autonomous vehicles. For more about the hardware and software that makes them work, see our Autonomous Driving Technology page.


What is autonomous driving?

The idea of driverless cars has been around for decades. But the cost to roll out the technology at scale has been prohibitive. With cheaper manufacturing for key components and advances in AI – driverless cars might be here sooner than you think.

Vehicles with limited automated driving features are already in use on-road today. Cruise control, lane guidance systems and emergency breaking are all current examples. These features require complete human supervision of the vehicle. The person in the driver’s seat is still “the driver” and automation supports the driving task.

In the future, technology may advance to a level where the vehicle manages the entire driving task. At that point the people sitting in the driver’s seat will simply be passengers. This could lead to automation being used for a range of society’s transport needs – replacing human drivers completely.

Fully autonomous transport systems might be years if not decades away. Researchers, industry, and governments are already working to understand the legal, safety and ethical considerations around our increasingly autonomous future.

What are the levels of driving automation?

The SAE Levels of Driving Automation TM is the industry’s most cited resource. The standard defines six levels of automation for motor vehicles and their operation on roads.

0No automationThe human driver performs all aspects of the dynamic driving task, even when enhanced by warning or intervention systems.
1Driver assistanceA driver assistance system executes specific tasks such as steering or acceleration / deceleration using information about the driving environment, with the expectation that the human driver performs all remaining aspects of the dynamic driving task.
2Partial automationOne or more driving assistance systems executes specific tasks such as both steering and acceeration / deceleration using information about the driving environment, with the expectation that the human driver performs all remaining aspects of the dynamic driving task.
3Conditional automationAn automated driving system performs all aspects of the dynamic driving task with the expectation that the human driver will respond appropriately to a request to intervene.
4High automationAn automated driving system performs all aspects of the dynamic driving task, even if a human driver does not repond appropriately to a request to intervene.
5Full automationAn automated driving system performs all aspects of the dynamic driving task under all roadway and environment conditions that can be managed by a human driver.

Vehicles with lower levels of automation are already available to buy and are legal to drive. Experimental vehicles with higher levels of automation are being tested on-road to ensure they are safe. Trials of vehicles with driverless features help autonomous vehicle researchers to understand how this technology might roll-out at scale.

What are the applications and benefits of autonomous driving?

Mobility innovation and economic opportunities presented by autonomy are poised to radically change the world. But researchers say we won’t use driverless cars and vehicles in the same way we do conventional ones. Autonomous driving has the power to transform the way cities work and how people move through them.

It’s thought that society would see fewer economic losses due to traffic congestion. People would be able to use their travel time more productively. This means more time in the day for people to work, study, socialise, engage in commercial activities and relax.

Travel would also become more accessible. People with mobility challenges within our current system would be able to travel more freely. Transport costs for transit systems may also come down – making services more efficient and affordable. The convenience of autonomous mobility as a service could also reduce passenger wait times.

The way vehicles look and behave will also change to move people and goods more efficiently. As increasingly automated systems are perfected it’s not just the transport and mobility sectors that will benefit. Mining and trucking companies are early adopters in the space – with early trials dating back to the 1990s. More recently, supermarkets, restaurants and local businesses have started to use sidewalk robots for unmanned delivery in trial basis.

The challenges of autonomous driving research

Technical, social, ethical and legal challenges must be addressed for progress to occur. Some experts are sceptical that true autonomous driving will ever be possible.

Others are working to resolve these issues in a collective effort to:

  • test and prove the technology
  • overcome critical safety concerns
  • consider human factors and mitigate social impacts
  • write laws and policies to support public safety

Autonomous vehicles and the changes ahead

Social and economic impacts

Widespread adoption of highly autonomous vehicles would transform the global marketplace. Millions of new jobs and immense economic opportunities would emerge. But people and companies relying on transportation industries would lose out.

For example, automated trucking could unlock exciting safety and economic benefits for some. It would reduce the impact of driver fatigue in crashes. With improved logistics consumers would get their stuff faster. But for this change to occur – millions of human truck drivers would lose their jobs. Massive economic flow-on effects would impact businesses supporting truck drivers. Motels, rest stops and local mechanics could all suffer too.

This is an ongoing concern across all industries facing the transition to increased automation. Policy researchers are calling for governments to understand and plan for negative impacts. A short-term fix might involve funding education and retraining programs for displaced workers. An example long-term solution could be the implementation of a universal basic income.

There are also thoughts that the institutional knowledge of drivers and logistics professional will add a lot of value to aiding automated freight and logistics vehicles operate safely and efficiently.

The change that is coming the way of this transport sector is something iMOVE is investigating in its Workforce implications of transport digitalisation and automation project.

Vehicle ownership and personal identity

Automation enables a range of transport options that may not have been possible in the past. It will also likely mean a change in the way society thinks about vehicle ownership in the future.

Individual vehicles owners may become far less common. Vehicle fleets may end up being owned and operated by private or public entities. This will likely meet resistance – due to the strong sense of identity and enjoyment some people associate with car ownership.

Vehicle ownership / use and traffic congestion

Vehicle ownership, and vehicle use, also has a strong bearing on hopes held that automated vehicles will help reduce traffic congestion.

On the positive side of looking at the introduction of autonomous vehicles on public roads, they may reduce traffic congestion in two key ways:

  1. They will ideally increase road safety and reduce crashes that result in significant traffic delays.
  2. They could reduce start-stop traffic created by human drivers – allowing for more efficiency even in congested conditions.

However, if ownership of autonomous vehicles replicates the current level of private vehicle ownership, there will be minimal change to our current congestion woes.

Worse still is the issue of what an autonomous vehicle does while it is not being used for trips with passengers. Does it park? Or does it drive around in circles awaiting the task of picking up its passengers? Clearly this behaviour would ramp up traffic congestion.

And there is some evidence on the negative influence of driverless vehicles on traffic congestion. In a Massachusetts Institute of Technology study, Impacts of transportation network companies on urban mobility, on the affect of Uber and Lyft vehicles in New York City. The result since the introduction of these services saw road congestion in terms of intensity increase by 0.9%, by duration a 4.5% increase, accompanied by an 8.9% decline in public transport use and an insignificant change in vehicle ownership.

Admittedly these figures stem from the addition of shared vehicles rather than autonomous vehicles, but they go some way to illustrating the possible consequences of change.

“It is far from clear how much of the congestion challenge can be resolved through greater sharing of private cars (no matter whether they are autonomous or not), increasing occupancy, assuming a constant number of person trips. However, sharing of private cars could lead to increased trips overall through a higher number of trips per vehicle, and to greater congestion if the number of trips overall goes up.” Says Professor David Hensher in Smart shared mobility and potential implications for levels of congestion.

Operational design domains: The behaviour of autonomous vehicles

The Operational Design Domain (ODD) articulates the most favourable conditions for autonomous systems and features to operate safely. It guides how autonomous vehicles are designed, built, tested and regulated.

Features intended to support Level 1 or 2 automation can only operate under very specific conditions. Their ODD is limited to those conditions and operating outside of them would be unsafe. For example, intelligent parking systems can only take over if your vehicle is close to a suitable parking space.

Level 5 vehicles would be able to operate under all driving conditions. Their ODD would be infinitely more nuanced and complex. A vehicle operating at this level would be guided by an ODD that defines conditions for a multitude of features, systems and processes.

Some factors the ODD considers:

  • Types of roads: for example, will features that support automation on highways or residential streets?
  • Topography: for example, will the vehicle operate on hilly terrain, flat urban roads or desert environments?
  • Speed constraints: low-speed autonomous shuttles typically operate at speeds below 15mph.
  • Ecological circumstances: for example – is it safe to operate the vehicle in rain or snow? Also does a particular feature perform better in daylight or at night?
  • Other specialised domain constraints: might include roadside infrastructure, traffic signs, laws and safety risks associated with a particular location or use case, and the co-existence of autonomous vehicles with legacy (non-autonomous) vehicles.


Human drivers make life and death decisions every time they drive. Autonomous vehicles will need to have the same ability for critical decision-making.

Ethics advocates believe it’s important to build ethics into the technology from the ground up. Thought experiments can help us to understand the complicated nature of this issue:

Imagine you are driving your car on the highway. You see something large fall off a truck in the lane ahead of you. You must swerve to avoid it. To your left is a family car and to your right is motorcycle with a single rider. Do you swerve left or right, or do you impact with the object on the road?

We may be able to accept a human’s imperfect reactions and decisions when collisions occur. But autonomous vehicles will be held to a higher standard. What types of objects should the vehicles crash into or avoid? Who should be making these decisions?

Autonomous vehicles’ decision-making algorithms are programmed by people. Are the humans who write decision-making algorithms responsible for fatal crashes? And, how to we avoid hard coding inherent human biases into software when the stakes are this high?

Laws and regulation

Policymakers and governments are already working to understand and regulate automated vehicle safety. The work of updating local, state and national laws to support widespread adoption will take years. Currently, many countries allow on-road trials under controlled conditions, including Australia.

Regional differences in laws may complicate roll-out of autonomous vehicles for global companies. Local laws and vehicle standards will mean a ‘one-size-fits-all’ approach will not work for manufacturers. Roll-out may be further delayed in some countries if their laws vary significantly between local jurisdictions.

Adding to this challenge is the fact that technology continues to evolve. This means policymakers and governments are writing laws to regulate vehicles that don’t even exist yet.

The trolley problem

One term you might see mentioned in relation to the introduction of autonomous vehicles to public roads, and the attendant issues of ethics, laws, and regulations is the trolley problem. The video above outlines the hypothetical scenarios the trolley problem introduces, and its options and repurcussions.

See Trolley Dilemmas Shouldn’t Influence Self-Driving Policies, Experts Argue for discussion on the philisophical contract that is the trolley problem and its relevance in the specific instance of autonomous vehicles.

iMOVE, Department of Transport and Main Roads (Queensland), and QUT put an autonomous car on Queensland streets

ZOE2, is a Renault Zoe electric vehicle, modified with technology that takes the car up to a SAE Level 4 automated vehicle capability. It has trialled on the Mount Cotton test track, and also on the streets of Ipswich, Bundaberg, and Mt Isa, with members of the public taking the opportunity to take a ride in the car.

Read more about the project, its findings and recommendations, at Cooperative and Highly Automated Driving Safety Study.

Autonomous driving careers

If you’re interested in pursuing a career in this area of transport, our interview series Meet Smart Mobility Experts could help guide you.

In this series we interview a number of researchers, practitioners, department of transport executives and more. Amongst other things we cover their academic background, research activity, career progression, and more.

Amit Trivedi and ZOE2 - 875x460

We can expect Level 3/Level 4 consumer vehicles with limited Operational design domain, such as on highways, entering the market around 2026, albeit in a very limited number. We could potentially see robotaxi service provider entering Australian market, initially on trial basis.

There are also possibilities of connecting a container freight hub with a port through automated container platforms during off peak hours, thus expanding port’s capacity and utilising excess road capacity during off peak.

Amit Trivedi – Program Manager at Queensland’s Department of Transport and Main Roads’ (TMR) Cooperative and Highly Automated Driving pilot (CHAD)

Autonomous Driving resources

Here’s a selection of Australian strategy and project documents on the topic of autonomous driving.

Autonomous driving: Facts and figures

Australian economic impacts

From 2020 to 2070:

  • Reduced crash costs are worth $152 billion, with more than 8000 lives saved.
  • Reduced transport time costs for private car users as they no longer need to perform the driving task are valued at $583 billion.
  • Reduced business time costs are almost $250 billion, along with reduced commercial vehicle (including light commercial) and bus time costs of more than $700 billion. Together, business and freight time cost savings are valued at $962 billion, reduced fuel use amounts to $54 billion due to smoother driving, and light and heavy vehicle platooning for regional and long distance and travel.
  • Lower fuel use leads to less Greenhouse Gas (GHG) emissions worth $10 billion.


  • Having fewer roads on which CAVs can be used autonomously would reduce uptake and benefits;
  • Safety impacts may be proportionally smaller if CAVs struggle to drive safely in mixed settings or human drivers do not interact safely with CAVs;
  • Travel time savings may not be fully achieved with mixed traffic if users do not feel trust and comfort in autonomous vehicles driving in mixed traffic;
  • Fuel savings rely on platooning and smooth travel through intersections, both of which are more difficult to achieve in mixed settings; and
  • Having more CAVs shared under subscription models (where multiple people pay to use but not own CAVs) would reduce vehicle costs. This would be a likely outcome where CAVs are expensive but have large benefits for users.

Source: The Economic Impacts of Connected and Automated Vehicles

Australia’s readiness for autonomous vehicles

  • According to KPMG’s 2020 Autonomous Vehicle Readiness Index, Australia came in at position 15, the same position as in the 2019 version of the index. 2020 was the past year this index was compiled and published. It assesses the AV readiness of 30 nations, and judging criteria includes policy and legislation, technology and innovation, infrastructure, and consumer acceptance. “Australia has strengths in its regulatory environment, the way autonomy is being considered in infrastructure projects and policies, and a range of trials are being undertaken across the nation. However, more could be done by companies and organisations involved in AVs to engage the public, in advance of the technology being widely available.”

Source: KPMG 2020 Autonomous Vehicle Readiness Index

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What is iMOVE doing in the area of autonomous driving?

iMOVE is carrying out R&D in a number facets around driverless vehicles. In a more top-down approach, in What C-ITS technologies for national deployment in Australia and Accelerating the uptake of C-ITS technologies in Australia we’re laying the ground for the introduction of autonomous vehicles in Australia by ensuring we select best technologies for our roads.

That’s a smart, important move, as is the mission of bringing the public along on this transport shift. Promoting community readiness and uptake of CAVs and Cooperative and Highly Automated Driving Safety Study. How do we educate the public on this mode, and how can governments increase community acceptance and confidence? We’re finding put!

Deeper dives into the technologies have been undertaken in HD mapping Australia’s CAV future, Development and use of cooperative perception for CAVs, and Improved sensing for signalised intersections.

Connected and automated vehicles are also important additions for vulnerable road users, and in this area we’re investigating issues and opportunities via 5G aid in automated mobility for elderly and people with disability, and Australia’s Public Transport Disability Standards and CAVs.

What impact iMOVE is having in the area of autonomous driving?

iMOVE and its partners are at the forefront of work in preparing for connectivity and automation of vehicles to help make Australian roads safer.

Our research and development is being used to understand automated driving in an Australian context, including readiness of our road assets (signs, lines etc) are for AVs, how drivers behave in response to AVs, how connected vehicle technologies can be integrated in automated technologies and the general performance of the technology.

While the work we’re doing is taking place in separate locations, what we are learning is highly applicable right across Australia.

Additionally, we’re readying Australia’s next generations of experts and practitioners to help make Australian roads prepare for driverless vehicles via our Undergraduate Student Industry and Industry PhD programs.

Contact iMOVE

There’s still a lot of work to be done to make Australian transport systems safer. If you’d like to talk to us about any R&D work in the area of autonomous driving please get in touch with us to start a discussion.

iMOVE autonomous driving projects

iMOVE, along with its partners, is active in carrying out R&D to advance autonomous driving technologies in Australia.

Please find below the three latest autonomous driving projects. Or click to view all iMOVE’s autonomous driving projects.


iMOVE autonomous driving PhD projects

In addition to iMOVE and its partners’ micromobility projects listed above, as part of our Industry PhD Program businesses, universities and PhD students work on an agreed topic over a three-year period.

These are the three most recent PhD projects that have been undertaken on the topic of autonomous driving. Click to view all iMOVE’s autonomous driving PhD projects.


iMOVE autonomous driving articles

In addition to projects, iMOVE also publishes articles, thoughtpieces, case studies, etc. that cover the many issues and solutions around autonomous driving.

Below are the three most recent articles. Or click to view all iMOVE’s autonomous driving articles.