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Autonomous cars use several sensors to detect objects such as pedestrians, other vehicles, and road signs.
However, bad weather, heavy traffic, road signs with graffiti on them all interfere with the accuracy of its sensing capability. To enable true autonomous cars, these sensors have to work in all weather conditions anywhere on the planet.
Currently, there is no widely accepted and agreed basis for ensuring that the machine learning algorithms used in the vehicles are safe.
Autonomous vehicles will use artificial intelligence and machine learning to process the data collected from its sensors to help make decisions about their next actions. The algorithms will help identify the objects detected by the sensors and classify them according to the system's training.
Once an autonomous car is on the road, it will continue to learn. It will detect new objects and be subject to software updates.
The system should continue to be just as safe as its previous version and not forget previously learned behaviors. It should be able to show that any new learning is safe. The industry still has to reach an agreement on these points.
Without recognized regulations and standards, self-driving cars will not be allowed on the road.
Currently, there are no sufficient standards and regulations for a whole autonomous system. The criteria for the safety of existing vehicles assume the presence of a human driver to take over.
Social acceptability is an issue for those wishing to buy a self-driving car and others who will share the road as numerous automated and autonomous vehicles have been in high-profile accidents.
There is a risk of rejection of this technology if the public is not considered in the decisions about the introduction and adoption of self-driving vehicles. Without collaboration on how to make the car safe and provide evidence of that safety, this project will not gain traction.
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