The Advanced Driver Assistance System (ADAS) holds a lot of promise. Many time the headlines about the autonomous vehicle (AV) industry, including the focus on accidents, control or company evaluation, seem unfortunate some.
None of this is unreasonable, but it makes the amazing possibilities of the world of AVS seem opaque. One of the universally accepted incentives for AVS is the potential positive impact on the environment, as most AVOs will be electronic vehicles (EVs). Industry analysts report that the project will achieve 7.3 million vehicles (7% of total market) of autonomous driving capacity by 2023, which would require $1.5 billion of autonomous-driving-dedicated processors.
Over 50% of all vehicles sold as defined by the National Highway Traffic Safety Administration (NHTSA) will be classified as SAE Level 3 or higher while in 2030 it could increase to $14 billion. Although photonic chips are faster and more energy efficient, fewer chips will be needed to reach SAE level 3; However, we can expect this enhanced computing performance to accelerate the development and availability of fully SA Level 5 autonomous vehicles.
In that case, the market for autonomous driving photonic processors will probably surpass the $14 billion project by 2030. When you consider all the broad-based potential uses of autonomous electric vehicles (EVs) – including taxis and service vehicles in the big cities, or the clean transportation of our highway goods – we begin to see how this technology can start to grow significantly faster. Affects our environment: Helps bring clean air the most populous and polluted cities.
The problem is that EVs currently have a sustainable problem. To operate efficiently and safely, EVs must be named after a degassing array of sensors: camera, leader, radar, and ultrasonic sensors. These work together, collecting data for real-time detection, feedback and forecasting, essentially becoming “eyes” for the car. During some debate surrounding the number of sensors needed to ensure effective and secure AV, one thing was unanimously agreed upon: these cars would generate a lot of data.
The response to the data generated by these sensors, even in the simplest way, requires a large amount of computing power – not to mention the battery power required for the sensors themselves to operate. Involved in deep learning algorithms in data processing and analysis, it is a branch of AI notorious for external carbon footprint. As an efficient efficiency alternative, the range of gas-powered vehicles for EVs needs to be matched, both in terms of energy efficiency and economy. However, the more sensors and algorithms an AEV has going on a journey, the lower the car’s battery range – and driving range -.
