Global and U.S. AI Chips for Self-Driving Market Report, Published by QY Research.

QY Research has released a comprehensive new market report on AI Chips for Self-Driving, high-performance computing processors designed to handle perception, sensor fusion, decision-making, and vehicle control in autonomous and advanced driver-assistance systems (ADAS). These chips integrate CPU, GPU, NPU/AI accelerators, and safety subsystems to process massive data streams from cameras, LiDAR, radar, and ultrasonic sensors in real time. As global automakers accelerate toward Level 2+ to Level 4 autonomy, this report provides an in-depth assessment of market size, architectural evolution, competitive landscape, and technology roadmaps shaping the future of autonomous mobility.

https://www.qyresearch.com/reports/5553847/ai-chips-for-self-driving

Core Market Data

Global market size: USD 10.5 billion
CAGR (2024-2030): 18.9%
Average price: USD 310 per unit
Annual production: 34 million units
Gross margin: 52%
Production capacity: 45 million units

List of Main Players

NVIDIA (NASDAQ: NVDA, USA)
Qualcomm (NASDAQ: QCOM, USA)
Intel (NASDAQ: INTC, USA)
AMD (NASDAQ: AMD, USA)
Tesla (NASDAQ: TSLA, USA)
NIO (NYSE: NIO, China)
XPeng (NYSE: XPEV, China)
Rivian (NASDAQ: RIVN, USA)
Samsung (KRX: 005930, South Korea)
Broadcom (NASDAQ: AVGO, USA)
NXP (NASDAQ: NXPI, Netherlands)
Renesas (TSE: 6723, Japan)
Black Sesame Technologies (China)
Tenstorrent (Canada/USA)
BOS Semiconductors (South Korea)
Hailo (Israel)
Syntiant (USA)
Graphcore (UK)

1. By Computing Architecture

CPU-centric AI Chips
GPU-based AI Chips
NPU-centric AI Chips

2. By Performance Class

Low AI Compute (Below 10 TOPS)
Mid AI Compute (10-100 TOPS)
High AI Compute (100-500 TOPS)
Ultra-high AI Compute (Over 500 TOPS)

3. By Process Technology

16 nm / 12 nm automotive-grade nodes
7 nm / 5 nm advanced logic nodes
Chiplet-based and multi-die architectures

4. By Application

Passenger Vehicles
Commercial Vehicles
Robotaxis

Case Study for Bidding

Time: May 2024
Supplier: NVIDIA
Buyer: Mercedes-Benz Group
Product: DRIVE Orin-based autonomous driving AI computing platforms
Quantity: 520,000 units
Contract Value: USD 220 million
Delivery Timeline: Q1 2026

Case Study for End Using

End User: Tesla

Application: Deployed in Full Self-Driving (FSD) computer systems for real-time perception and driving decision-making.

Installation Location / Usage Stage

Integrated into centralized vehicle compute modules.
Connected to multi-camera, radar, and ultrasonic sensor arrays.
Operates continuously under automotive safety and thermal constraints.

Purpose

Executes deep neural networks for object detection and tracking.
Enables real-time sensor fusion and path planning.
Supports over-the-air software updates and AI model upgrades.

Effect

Improved perception accuracy by 30%+ versus prior-generation platforms.
Reduced system latency for high-speed decision making.
Enabled scalable deployment of advanced autonomy features.

Alternative Computing Solutions
Traditional automotive MCUs are reliable but lack AI performance.
Edge GPUs provide flexibility but consume higher power.
Cloud-based processing supports training but cannot meet real-time driving latency requirements.

Therefore, AI Chips for Self-Driving have become the core enabling technology for autonomous vehicles-balancing performance, power efficiency, safety certification, and long-term software scalability.

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