The automobile 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 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 Level 3 autonomous driving.

The concept of L3 Automation Driving – 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. All of this must function in unison to ensure that electronic control units can make and perform judgments in the fraction of a second it takes for a vehicle to slam on its brakes or a child to stroll into the street.

Automation – The Irony

When a system relies on both automation and human supervision, L3 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.

The solutions?

Connecting cars with other vehicles and infrastructure so that they are always communicating with each other could be part of the solution for safe Level 3 but also Level 4 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, but also more networked self-driving cars.

This connection could reduce the hazards associated with a Level 3 vehicle 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 will 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.

Social acceptability

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.

Whatever happens in the future, autonomous vehicles will profoundly transform the way we travel as individuals shift from the driver’s seat to the passenger seat. What was formerly only seen on TV in movies is about to become a reality.

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