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Performance of autonomous cars to rely heavily on automotive sensor-cleaning technology

12-17-2021 08:39 AM CET | Advertising, Media Consulting, Marketing Research

Press release from: Netscribes

Performance of autonomous cars to rely heavily on automotive

Netscribes, in its latest whitepaper, analyzes the state of automotive sensor cleaning technology and its impact on the future of autonomous vehicles

New York, December 17, 2021 – Netscribes, Inc., "https://www.netscribes.com/"a global data and insights provider to leading automotive companies, has published its findings on Automotive Sensor Cleaning Technology "https://www.netscribes.com/insights/the-state-of-automotive-sensor-cleaning-technology/". In this whitepaper the research firm examines the inception of sensor cleaning technology in cars, driven by the adoption of autonomous technologies across different vehicle classes.

Sensors feed vehicle systems with important environmental data, enabling automated and autonomous operations. However, since vehicles are constantly exposed to various kinds of weather and road conditions, these sensors often get obscured due to heavy rain, snow, mud, bird droppings, dead bugs, and different types of debris.

To address this challenge, industry players are exploring four fundamental types of cleaning technologies: Wipers and jets, protective material, passive/active aerodynamics, and advanced technologies, including ultrasonic and cavitation.

Even though sensor cleaning technology is still being developed, autonomous vehicle OEMs face the challenge of ensuring that the sensors are integrated properly, maintained well, and are never compromised, failing which, accidents may take place.

Given the crucial role sensors play in enabling accurate data inputs, navigation, and time-sensitive autonomous communication, knowing what’s next in this space will be key for makers of autonomous vehicles.

With the adoption of new and emerging variants such as optical sensors, LIDAR, camera sensors, and radar sensors, several ADAS and autonomous vehicle engineers are on the lookout for efficient sensor-cleaning solutions. This demand is expected to drive the automotive sensor market, helping it to reach 23.6 billion units globally, by 2034.

“It will be interesting to see how the industry will evolve,” said Siddharth Jaiswal, an automotive expert at Netscribes. “OEMs expect sensor makers to provide an integrated cleaning solution, while the latter consider their business to be purely sensor manufacturing. Tier-1 sensor suppliers are still trying to figure out possible solutions, while non-auto companies in the field of medical devices and photography cameras are evaluating the market potential.”

The major takeaways from this whitepaper include:

Information about key players, research institutes, and universities associated with the current automotive sensor cleaning ecosystem

The current state of technologies in this space and changes anticipated in the future

Patent filing trend and publication landscape

It also delves into potential technologies that can solve sensor cleaning challenges and offers insight into specific technological combinations that can work best at various autonomy levels. It sheds light on the latest market developments, providing a tangible perspective into where leading players stand on the path to commercialization.

Netscribes
41 East, 11th Street, New York
NY10003, USA

Netscribes is a global data and insights firm that uses data to meet the innovation needs of leading automakers and OEMs to identify growth opportunities and to understand the impact of new and emerging technologies. The company specializes in gathering data from varied sources, translating it into meaningful information, insights, or content, and using it to enhance customer journeys. Over the last two decades, Netscribes has helped Fortune 500 companies, as well as high-potential startups, leverage data to tackle disruptions, understand evolving customer needs, and accelerate business growth. To learn more, please visit www.netscribes.com.

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