Tesla vs. BYD vs. XPeng: Analysis of the Latest Intelligent Driving Architectures

As the race toward fully autonomous driving intensifies, Tesla, XPeng, and BYD have each adopted distinct technical paths. This article compares their latest intelligent driving architectures in terms of hardware configurations, software strategies, and technical innovations.

Tesla: Vision-Centric End-to-End Architecture

Hardware Architecture
Tesla’s latest platform, HW4, is equipped with eight surround-view cameras and its in-house FSD (Full Self-Driving) chip, boasting hundreds of TOPS in compute power. While Tesla briefly removed millimeter-wave radar, it has recently reintroduced it in some configurations.

Software Strategy
Tesla's FSD relies on an end-to-end BEV (Bird’s Eye View) occupancy network, which stitches multi-camera semantic information into a top-down view. From this, the system predicts drivable areas and generates motion trajectories—entirely through neural networks and without relying on high-definition maps.

Technical Features

· End-to-End Design: Simplifies system architecture through joint neural optimization.

· Data-Driven: Model improvements are driven by vast amounts of real-world driving data and frequent OTA updates.

· High Opacity: Tesla’s decision-making is largely opaque due to its reliance on deep neural networks.

XPeng: Multi-Modal Fusion and Large AI Models

Hardware Architecture
The XPeng G9, for example, is powered by dual Nvidia Orin chips (508 TOPS total), 12 cameras, 5 mmWave radars, 12 ultrasonic sensors, and 2 LiDARs—enabling high-resolution, multi-modal sensing.

Software Strategy
XPeng's system integrates multiple large AI models:

· XNet: A unified deep visual perception network capable of 3D environment reconstruction.

· XPlanner: A neural network-based motion planner for smoother and more human-like driving behaviors.

· XBrain: Uses a large language model to enhance contextual understanding and reasoning.

Technical Features

· Mapless Navigation: XPeng’s XNGP system operates without high-definition maps, relying solely on onboard sensing and AI inference.

· Rapid Iteration: With large datasets and compute power, XPeng expects a 30x system performance improvement within 18 months.

· Multi-Modal Fusion: Integrates visual, spatial, and semantic information for better generalization.

BYD: Intelligent-Electric Fusion via the “Xuanji” Architecture

Hardware Architecture
BYD’s “Xuanji” (璇玑) architecture fuses electrification with intelligent systems. It includes:

· One Brain: A central control chip governing vehicle intelligence.

· Two Ends: Cloud AI and in-vehicle AI working in concert.

· Three Networks: Integration of V2X, 5G, and satellite communication.

· Four Chains: Integration of sensing, control, data, and mechanical systems.

Software Strategy
BYD categorizes its driving assistance into levels:

· DiPilot: Level 2 driver-assistance system (DiPilot 10 and 30).

· “Tienshen Eye” (Heavenly Eye): Level 2+ advanced driving system (DiPilot 100/300/600), with naming based on compute capacity.

Technical Features

· Full-Stack In-House Development: Combines key BYD technologies like Yisifang, DMO, e-Platform, and Dilink.

· Multi-Modal AI Foundation Models: Covers over 300 driving scenarios using proprietary data and AI training infrastructure.

· Real-Time Perception and Response: Achieves millisecond-level sensing and decision-making via deep neural networks.

Summary Comparison

Wrapping Up

Tesla leads with a minimalist, vision-first strategy that simplifies architecture but leans heavily on data scale. XPeng adopts a more diversified approach with rich sensor inputs and large foundation models to improve generalization and safety. BYD emphasizes integration between intelligent systems and electric platforms, reflecting its strength in vertical integration and control over the entire supply chain.

Each company is pushing toward autonomy, but with unique philosophies—Tesla through software scale, XPeng through model sophistication, and BYD through holistic system design.