Although autonomous technologies haven't reached wide scale adoption yet, many have already been tested and protoyped. With deployment imminent, we analyzed the four largest technical challenges facing autonomous vehicles today. Developing solutions to tackle these specific challenges will be compulsory to accelerate the deployment of level 5 automation.
The drive towards autonomous vehicles will encounter many challenges and several of them will revolve around data. The scale of the task of creating a car which can drive more reliably than a human cannot be underestimated. In just one day, a test autonomous vehicle produces as much data as the Hubble telescope produces in a whole year. Many companies involved in the development of self-driving cars have turned to deep learning techniques to reach their goals, which means analyzing vast amounts of data from many vehicles. Moreover, this data must be stored and managed effectively in order to train the neural networks which will eventually control autonomous vehicles.
Autonomous vehicles have gone beyond a concept to being accepted as the future of mobility, but the motor industry has plenty to grapple with in order to make that a reality. High profile accidents involving autonomous cars have highlighted that various technical barriers still remain in terms of scalability and deployment. Consumer confidence is essential to the introduction of self-driving vehicles, while regulators at state and federal level will require robust safety protocols.
The Autonomous Vehicles Summit surveyed 325+ global automotive leaders to identify where car manufacturers are on their journey towards autonomous, how they’re capitalizing on this opportunity for reinvention and the key challenges they’re facing as a result. This report aims to not only present a snapshot of the current state of autonomous, but also enable the development of a new set of industry benchmarks, best practices and innovative solutions.