Autonomous vehicle chips help process and interpret vast amounts of sensor data in real-time to detect obstacles, predict trajectories and navigate safely. Key functions include lane keeping, adaptive cruise control, automated parking and fully autonomous driving. As autonomous features become more sophisticated, the computing power demands on chips are rising exponentially.
The Global Autonomous Vehicle Chips Market is estimated to be valued at US$ 23.64 Bn in 2024 and is expected to exhibit a CAGR of 8.5% over the forecast period from 2024 to 2031.
Autonomous driving is increasingly being adopted to reduce road accidents caused by human errors. The rise of advanced driver assistance systems and growing demand for safer, more efficient mobility options are major factors driving the need for autonomous vehicle chips.
Key Takeaways
Key players operating in the Autonomous Vehicle Chips market are ABB Ltd, Infineon Technologies AG, Intel Corporation, MobilEye (an Intel company), NVIDIA Corporation, Qualcomm Incorporated, Renesas Electronics Corporation, Samsung Electronics Co., Ltd., Siemens AG, Texas Instruments (TI), Tesla, Inc., Waymo LLC, Xilinx, Inc., Aptiv PLC, and Aurora Innovation, Inc. These leading chip manufacturers are focused on developing more powerful and specialized processors to enable full autonomy.
Growing vehicle electrification and the use of advanced driver assistance systems are fueling demand for autonomous vehicle chips. Applications ranging from adaptive cruise control and automated emergency braking to traffic jam assist and fully autonomous driving require enhanced processing capabilities.
The Autonomous Vehicle Chips Market Demand is expanding globally with the rise of robotaxis and self-driving trucks. Countries worldwide are supporting R&D in autonomous technologies to drive road safety as well as commercial advantages. Collaboration between tech and automotive firms is accelerating the adoption of autonomous mobility solutions across major economies.
Market Key Trends
The use of Edge computing is a key trend in the autonomous vehicle chips market. As sensors generate terabytes of data per hour, processing this vast amount of data in real-time requires distributed computing capabilities. Edge computing deployed directly in vehicles helps overcome bandwidth and latency issues associated with cloud-based approaches. This enables critical functions like emergency braking to be performed locally without delays.
Porter’s Analysis
Threat of new entrants: Entry barriers for semiconductor manufacturing are very high due to requirement of large capital investments and technical expertise.
Bargaining power of buyers: Buyers have moderate bargaining power due to less product differentiation in autonomous vehicle chips market segment.
Bargaining power of suppliers: Suppliers have moderate bargaining power due to presence of several suppliers for raw materials required for semiconductor manufacturing.
Threat of new substitutes: Threat of substitutes is low as autonomous vehicles require specialized semiconductors that provide enhanced processing power and capabilities.
Competitive rivalry: Intense competition exists among key players to gain market share through innovations and new product developments.
Geographical Regions
Asia Pacific region accounts for the largest share of the global Autonomous Vehicle Chips Market Size And Trends due to presence of several automotive chip manufacturers in countries like China, Japan and South Korea. Rapid increase in demand for autonomous and electric vehicles from automakers in this region further drives the market growth.
North America region is expected to witness the fastest growth during the forecast period due to increasing investments by automakers as well as chip manufacturers in developing autonomous driving technologies. Favorable government initiatives to support R&D and testing of autonomous vehicles also boost the autonomous vehicle chips market in countries like U.S. and Canada.
*Note:
1.Source: Coherent Market Insights, Public sources, Desk research
2.We have leveraged AI tools to mine information and compile it
About Author - Money Singh
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