"Automotive technology has evolved significantly, and it's widely accepted that autonomous driving will bring a major transformation to the industry. However, as various technologies develop, different stakeholders—including major automakers, internet companies, research institutions, telecom firms, and tech giants—have diverse opinions on the timeline and direction of this evolution. This article references a study by Monita Caixin Think Tank, which systematically outlines the key technological milestones in autonomous driving and their expected maturity timelines."
**Key Points**
1) **Automotive Electronics: Two Horizontal and Three Vertical Technical Frameworks**
The development of intelligent and networked vehicles is progressing through a two-horizontal-and-three-vertical technical architecture. From 2016–2018, the focus was on sensor fusion (LiDAR, radar, and vision). By 2017–2019, high-precision maps began to mature. From 2019–2022, in-vehicle communication modules and services advanced. By 2022–2025, decision chips and algorithms reached maturity.
2) **2016–2018: Sensor Fusion**
Domestic millimeter-wave radar started shipping, while vehicle vision systems reached consumer levels. The cost of LiDAR decreased, accelerating the adoption of ADAS and autonomous driving features.
3) **2017–2019: High-Precision Maps**
Traditional maps couldn’t meet the demands of autonomous driving. High-precision maps became essential for L3 and L4 levels. Key players included map providers, AI-driven companies, and traditional automakers. Each had unique strengths and challenges, with hardware and software integration gradually taking shape.
4) **2019–2022: In-Vehicle Communication Modules**
LTE-V showed advantages in latency, bandwidth, reliability, and cost. It was expected to become the standard. Companies like Datang Telecom introduced LTE-V car networking equipment, signaling rapid growth in this area.
5) **2022–2025: Decision Chips and Algorithms**
Major manufacturers used custom chips to support different algorithms. Google developed TPUs for CNN acceleration, while Intel acquired Mobileye to integrate chip and algorithm design. Future developments may rely on FPGAs for deep learning-based autonomy.
**1. Automotive Electronics Development Timeline**
**1.1 Intelligent and Networked Vehicle Architecture**
Intelligent and networked vehicles use advanced sensors, controllers, and actuators, integrating modern communication and network technologies. This enables information exchange between vehicles and their surroundings, supporting complex environmental awareness, intelligent decision-making, and coordinated control. The technical framework is based on two horizontal and three vertical architectures.
**1.2 Development Timeline: Intelligent and Networked Synergy**
From 2016–2018, sensor fusion enabled partial automation (PA) functions such as adaptive cruise and emergency braking. From 2017–2019, high-precision maps improved lane-keeping and parking. From 2019–2022, 5G and V2X accelerated network connectivity for more complex scenarios. By 2022–2025, decision chips and algorithms supported full autonomy.
**1.3 Policy Support for ADAS**
European policies mandated safety technologies like automatic emergency braking and lane departure warnings. In China, similar regulations pushed automakers to adopt ADAS components, boosting the automotive electronics market.
**1.4 Global Players: Google and Tesla**
Google focused on mapping and deep learning, while Tesla relied on sensor fusion. Google’s Waymo achieved significant progress in autonomous systems, while Tesla’s Autopilot 2.0 featured enhanced cameras, sensors, and processing power.
**1.5 Domestic Innovation in Autonomous Driving**
Chinese universities and companies made progress in developing ADAS prototypes. Tsinghua University and others created systems for adaptive cruise control and lane departure warnings, moving toward industrialization.
**2. 2016–2018: Sensor Fusion**
**2.1 Millimeter-Wave Radar in China**
Millimeter-wave radar shifted from high-end to low-end models. 24GHz and 77GHz bands were used for rear and front detection, respectively. Foreign companies dominated 77GHz MMIC, but domestic companies like Huayu started production.
**2.2 Car Vision Systems**
Camera hardware reached maturity, while software upgrades improved performance. Mobileye upgraded its chips and image resolution, enhancing detection capabilities.
**2.3 LiDAR Cost Reduction**
LiDAR, combining laser, GPS, and inertial navigation, provided high-precision 3D data. While foreign companies led in multi-line LiDAR, Chinese firms were still in early stages of development.
**3. 2017–2020: High-Precision Maps**
**3.1 Traditional Maps vs. High-Precision Maps**
High-precision maps offered centimeter-level accuracy and richer traffic data. They were critical for L3/L4 autonomy, reducing reliance on other sensors and improving reliability.
**3.2 Map Layers and Data Integration**
High-precision maps had active, dynamic, and analysis layers. They supported real-time updates and collaborative decision-making, enhancing autonomous driving capabilities.
**3.3 Industry Participation**
Map providers, AI companies, and automakers played key roles. Collaboration between these groups was expected to drive industry growth.
**3.4 Global Trends and Technological Convergence**
Companies like HERE focused on location services and data integration. Google and Apple emphasized real-time updates and scalability, positioning themselves as leaders in high-precision mapping.
**3.5 Future of High-Precision Maps**
According to the "Made in China 2025" roadmap, high-precision maps would reach sub-meter accuracy by 2020, supporting full autonomy by 2030.
With the advancement of AI and 5G, high-precision maps are set to become even more integral to autonomous driving, enabling safer, smarter, and more efficient transportation systems."
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