The automotive industry is striving to produce the much-anticipated self-driving cars that will not require any human interaction. Many businesses are pushing toward the day when people will be able to entirely detach from the driving process, but they disagree on some levels on how to do so securely. Some manufacturers are deploying autonomous driving technologies like adaptive cruise control and obstacle avoidance one at a time. Others believe that any self-driving automobile on public roads should not require any human assistance.

However, because of the complications of blending automation technology with human interaction, there appear to be some difficulties with the levels of autonomous driving.

The Society of Automotive Engineers (SAE) has established six distinct levels of driving automation, ranging from Level 0, which involves no automation at all, to Level 5, representing complete autonomy.

Let’s explore each level in detail

Levels of autonomous driving

Level 0: no driving automation

Many cars on the road today are manually operated, or Level 0. The dynamic driving task is performed by humans, even though there might be devices in place to assist the driver.

Level 1: driver assistance

This is the entry-level of automation. The car has a single autonomous system that helps the driver with tasks like accelerating and steering (cruise control). Because the driver is in charge of steering and braking in addition to keeping the automobile at a safe distance behind the following car, adaptive cruise control is considered a Level 1 feature.

Level 2: partial driving automation

At Level 2, we encounter advanced driver assistance systems (ADAS). The car is capable of steering as well as accelerating and decelerating. Because a human is seated in the driver’s seat and has the ability to take control of the vehicle at any time, this automation falls short of being fully autonomous.

Level 3 – conditional driving automation

Conditional driving automation is the third level of automation. It makes judgments based on changing driving scenarios around the vehicle using multiple driver aid systems and artificial intelligence. People within the car are free to do other things because they are not required to monitor the technology. However, in the event of a system failure, a human driver must be there, aware, and capable of taking control of the vehicle at any time.

Producing an L3 vehicle involves the use of advanced technology, software, algorithms, and a massive amount of data. Everything must work together smoothly so that the electronic control units can quickly make decisions when a vehicle needs to brake suddenly.

Level 4 – advanced automated driving

One of the main distinctions between Level 3 and 4 is the ability of Level 4 vehicles to step in in the event of a malfunction or system breakdown. In this way, most of the time these cars aren’t required to communicate with people. A human can still manually override, though.

Autonomous driving is possible for Level 4 vehicles. However, they can only do so in a restricted area (often an urban setting where top speeds average 30 mph). At least until laws and infrastructure change. This is sometimes referred to as geofencing.

Level 5 – full driving automation

At Level 5, vehicles operate without the need for human attention, completely eliminating the dynamic driving task. These cars will lack traditional controls like steering wheels and pedals. They will not be restricted by geofencing and will have the capability to travel anywhere and perform all tasks typically handled by experienced human drivers. While fully autonomous cars are undergoing testing in various locations worldwide, they are not yet accessible to the general public.

Automation – The Irony

When a system relies on both automation and human supervision, autonomous driving presents particular safety concerns. The irony of automation refers to the reality that an automated system that requires monitoring can keep a human driver’s attention, whereas a system with more autonomy will eventually lose the driver’s attention, making a re-entry into the supervisory loop much more difficult.

Social acceptability stands out as a main challenge

Several high-profile accidents involving Tesla’s existing automated vehicles, as well as other automated and autonomous vehicles, have occurred. Social acceptability is a concern not only for people who are considering purchasing a self-driving car. But also for those who share the road with it.

The public must be included in decisions about self-driving vehicle introduction and adoption. Without it, we risk having this technology rejected.

The solutions?

autonomous vehicles

Connecting cars with other vehicles and infrastructure so that they are always communicating with each other. This could be part of the solution for safe Level 3 but also Level 4 and 5 autonomy. Another backup would be in place if cars and traffic-control systems could receive continual updates over a secured wireless connection. Oncoming traffic may be predicted from miles away, and motorists could be warned of hazards ahead by vehicles considerably closer to the scene.

A major issue for self-driving cars is predicting what other automobiles will do next. The solution is not simply more self-driving cars on the road. Rather it involves improving the connectivity among these autonomous vehicles. This connection could reduce the hazards by allowing the driver greater time to take control before an accident occurs.

The automotive industry is actively working on fail-operational safety architectures for systems with the powertrain in the context of the Advanced Driver Assistance System and Autonomous Driving processing chain – from sensors to perception and decision algorithms. This practice has directed to architectures incorporating hardware and process redundancy, real-time error detection, masking, and advanced reconfiguration to maintain normal operations after a fault.

Machine Learning and Artificial Intelligence

Artificial intelligence and machine learning is used and will continue to be used by most autonomous vehicles to interpret data from sensors and assist in making judgments about their next actions.

These algorithms will help in identifying the elements observed by the sensors and classifying them as a pedestrian, a street light, and so on, based on the system’s training. The automobile will then use these results to analyze whether it has to take action to prevent a detected obstacle, such as braking or swerving.

Machines will be able to recognize and classify objects more efficiently than human drivers in the future. However, there is currently no widely established and consented foundation for verifying the safety of machine learning algorithms used in automobiles.

According to analysts, there will be an estimated 21 million autonomous cars on the roads by 2035.

Whatever happens in the future, autonomous vehicles will transform the way we travel as individuals. What was formerly only seen on TV in movies is about to become a reality.


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