In today's rapidly evolving technological era, autonomous driving technology is undoubtedly one of the most talked-about topics in the automotive industry and intelligent transportation systems. It not only heralds a fundamental transformation in future travel modes but also serves as a crucial cornerstone for the development of smart cities. In this field, Tesla's Full Self-Driving (FSD) system and China's Vehicle-Road-Cloud Integration solution are leading two powerful trends in global autonomous driving technology.

Tesla FSD: An Explorer of End-to-End Large Models

Tesla's Full Self-Driving (FSD) system, as a cutting-edge exploration in autonomous driving technology, is full of innovation and challenges. The design philosophy of FSD lies in creating a closed-loop, end-to-end learning and decision-making system. This means that the vehicle must not only have the ability to perceive the environment but also understand this information and make driving decisions accordingly, ultimately executing operations, all within the vehicle itself without relying on external infrastructure assistance.

The overall technical architecture consists of several components

1. Sensor Fusion

The core of FSD starts with its sensor suite distributed throughout the vehicle body, including: Cameras: Eight cameras surrounding the vehicle provide a 360-degree view, capturing high-resolution images for identifying road signs, pedestrians, other vehicles, etc. Radars: Forward-facing radars detect distant obstacles, especially providing supplementary information in adverse weather conditions. Ultrasonic Sensors: These sensors are primarily used for detecting close-range obstacles, such as measuring distances while parking. GPS and Inertial Measurement Unit (IMU): Provide precise location and orientation information. 2. Data Processing and Learning

The collected raw data is sent to Tesla's self-developed FSD computer (formerly known as Hardware 3 or HW3), a high-performance computing platform specifically designed for autonomous driving. Several key steps occur here:

Preprocessing: Data is first cleaned and formatted, preparing it for further analysis.

Perception Layer: Deep learning models, such as Convolutional Neural Networks (CNNs), are used to analyze images, identifying object categories, locations, speeds, etc.

Prediction and Planning: Based on perception results, the system needs to predict the behavior of surrounding objects and plan the vehicle's driving trajectory accordingly. This includes complex decision tree algorithms and reinforcement learning models, enabling the vehicle to respond to various possible scenarios.

Control Execution: Finally, based on the planning results, the system sends instructions to the vehicle's steering, acceleration, and braking systems to achieve automated operations.

3. End-to-End Training and Continuous Learning

One of the key features of FSD is its end-to-end training method, which allows the system to be optimized directly from raw sensor inputs to final driving action outputs, reducing the need for intermediate manually defined features. Furthermore, Tesla utilizes its vast fleet to collect real-world driving data, which is uploaded to the data center via the cloud for continuous training and improvement of its algorithms. This "shadow mode" allows FSD to test new strategies without affecting actual driving, while the optional "beta" program further accelerates the collection of real-world feedback.

Despite demonstrating strong technical potential, FSD still faces challenges related to technical maturity, regulatory frameworks, and other issues.

Vehicle-Road-Cloud Integration: An Upgraded Version of Single-Vehicle Intelligence

Unlike Tesla, which focuses on vehicle intelligence, China has proposed a grander vision in the field of autonomous driving: Vehicle-Road-Cloud Integration. This concept emphasizes the deep integration and collaborative operation between vehicles, road infrastructure, and cloud data processing platforms. In short, vehicles are no longer isolated intelligent agents but are integrated into a larger intelligent transportation ecosystem. Intelligent facilities deployed on roads, such as AI digital road base stations and V2X communication devices, can monitor road conditions in real-time, providing vehicles with beyond-visual-range perception. The cloud utilizes big data and AI algorithms to optimize traffic flow scheduling, achieving a more efficient and safer driving environment. Additionally, this system provides robust support for autonomous vehicles, enabling accurate judgments even in severe weather or complex traffic conditions with the assistance of the cloud platform.

Safety is crucial for autonomous driving. Single-vehicle intelligence cannot currently ensure true driving safety, whereas Vehicle-Road-Cloud Integration provides a god's-eye view. The cloud not only sees more comprehensively and farther but also calculates faster, significantly advancing the performance and functionality of single-vehicle intelligence.

The overall technical architecture consists of several components

1. Intelligent Road Infrastructure (Road)

Perception Base Stations and V2X Communication: The foundation of the Vehicle-Road-Cloud Integration system lies in the intelligent upgrade of roadside infrastructure, including high-precision sensors, cameras, millimeter-wave radars, and 5G/V2X communication base stations installed along the roadside. These facilities can capture real-time road conditions, traffic flow, meteorological information, etc., and directly communicate with vehicles via V2X technology to achieve beyond-visual-range perception, providing early warnings of potential hazards and richer environmental information for vehicles.

Dynamic Road Signs and Signal Control: Intelligent street lights, variable message signs, smart traffic lights, and other devices automatically adjust based on real-time traffic conditions, optimizing traffic flow, reducing congestion, and improving traffic efficiency.

2. Efficient Data Processing and Analysis Cloud Platform (Cloud)

Big Data and AI Algorithms: The cloud aggregates massive amounts of data from vehicles, road infrastructure, and other third-party sources. Using big data analysis and AI algorithms, it conducts deep learning and pattern recognition, conducts refined management of traffic flow, predicts traffic trends, optimizes route planning, and improves the overall efficiency and safety of the transportation system.

Edge Computing: To reduce latency and improve response speed, edge computing technology is applied for local data processing, performing preliminary processing close to where the data is generated before uploading and aggregating it to the cloud for rapid decision-making and command issuance.

3. Intelligent Integration of Vehicles (Vehicle) In-vehicle Terminals and Integrated Control Systems: Vehicles are equipped with Advanced Driver Assistance Systems (ADAS), high-precision positioning systems, and in-vehicle communication modules, enabling them to receive information from the cloud and road infrastructure while uploading their own status data. Through the intelligent decision-making system on the vehicle side, vehicles can respond to real-time changes in road conditions and automatically adjust driving strategies. Vehicle-Road-Cloud Integration provides a new approach to training large models for autonomous driving. Compared to traditional vehicle-based data collection, the data scale and richness of data types obtained through the Vehicle-Road-Cloud Integration system are significantly enhanced, greatly promoting the training of large models for autonomous driving.

Both paths have their merits

While Tesla's FSD and Vehicle-Road-Cloud Integration take different paths, their goal is the same – to achieve safer, more efficient, and convenient autonomous driving. FSD focuses more on the intelligence and self-learning ability of the vehicle itself, continuously improving the autonomous driving level of individual vehicles through technological iterations. In contrast, Vehicle-Road-Cloud Integration takes a system-level approach, building intelligent transportation infrastructure through overall planning to provide comprehensive support and services for every vehicle on the road.

Both models also face their respective challenges, such as data privacy, network security, and infrastructure investment costs. However, undeniably, they jointly drive rapid progress in autonomous driving technology, bringing us one step closer to the era of true intelligent travel.