Ankündigung • Jul 17

WeRide Unveils WITT, a Physical AI Cognitive Foundation Model Built on Atomic Physical Facts

WeRide unveiled WITT (World Intelligence Toward Truth), a Physical AI Cognitive Foundation Model designed to build AI cognition of the physical world through trusted facts extracted from real-world experience. WITT introduces Atomic Physical Facts (APFs), the smallest verifiable units of information about the physical world, establishing a new fact-based cognitive framework for Physical AI. Built on four core capabilities—Fact Extraction, Fact Reasoning, Fact Verification and Fact Curation—WITT continuously transforms real-world data into trusted learning signals for AI training, evaluation and iteration. Compared with significantly larger general-purpose AI models, WITT reduces token costs by up to 98% and delivers up to 200x greater data-processing efficiency. Leveraging visual-language model (VLM) capabilities, WITT introduces a new concept called Atomic Physical Facts (APFs) and establishes a fact-based cognitive framework for Physical AI. By connecting multimodal information across video, images and text, WITT decomposes continuously evolving real-world environments into verifiable facts that can be identified, reasoned about and validated, establishing a new generation of AI understanding centered on physical facts. WITT stands for World Intelligence Toward Truth and is inspired by the philosopher Ludwig Wittgenstein, whose proposition that “the world is the totality of facts” closely aligns with the underlying logic of Physical AI. To build cognition of the physical world, AI must first identify trusted facts embedded in environments, behaviors, rules, risks and temporal relationships. These facts become the foundation for reasoning, judgment and decision-making. As Physical AI moves from research into real-world deployment, autonomous driving has emerged as the first domain to achieve large-scale commercial validation. Vast amounts of real-world data continue to grow exponentially, identifying and utilizing data with genuine training, evaluation and iteration value remains difficult. High-value long-tail scenarios are inherently scarce, while datasets collected from both L4 autonomous driving operations and production ADAS systems often contain human interventions, inactive segments and other forms of noise. General-purpose AI models can also struggle to interpret complex traffic environments consistently, leading to hallucinations, factual errors and incomplete scene understanding. The industry increasingly needs an efficient and trusted mechanism for understanding data—one capable of continuously extracting meaningful scene facts from real-world driving data, improving the quality and efficiency of training, evaluation and model iteration, and transforming real-world experience into trusted learning signals that drive the evolution of autonomous systems. WITT was developed to address this challenge. Rooted in WeRide’s large-scale autonomous driving operations, WITT continuously extracts patterns, relationships and trusted facts from vast volumes of operational data. Rather than treating data as raw inputs for model training, WITT treats trusted facts as the fundamental building blocks of Physical AI cognition. This foundation enables the model to transform real-world experience into structured knowledge through four core capabilities: Fact Extraction, Fact Reasoning, Fact Verification and Fact Curation. Together, these capabilities create a complete pipeline spanning scene understanding, event attribution, data validation and learning curation—allowing every kilometer of real-world driving data to become a trusted signal for model improvement. WITT identifies and extracts three categories of Atomic Physical Facts from real-world driving data: standard driving facts, multi-agent interaction facts and physically ambiguous conditions. Together, these facts capture everyday traffic behaviors, evolving relationships among traffic participants and uncertainty within complex physical environments. For example, a driving video can be decomposed into multiple Atomic Physical Facts, including reduced visibility caused by rain, a pedestrian entering a crosswalk, an ego vehicle slowing down, a nearby vehicle traveling in parallel, changing traffic signals and increasing collision risk. Each fact is designed to be highly reliable, traceable and verifiable, enabling richer scene descriptions and providing the foundation for subsequent reasoning, validation and learning. After extracting facts, WITT analyzes key events, behavioral relationships and evolving risks within a scene, while identifying the underlying causes and potential trajectories of those events. Powered by an integrated video intelligence engine, WITT enables users to retrieve target scenarios through keywords or natural-language queries, dramatically improving the efficiency of scenario discovery, data investigation and root-cause analysis. To reduce hallucinations commonly associated with general-purpose AI models, WITT evaluates outputs across six dimensions: Vulnerable road users, Ego-vehicle behavior, Surrounding vehicle behavior, Scene understanding, Comprehensive fact, Traffic facilities. The model introduces factual confidence scoring and validates conclusions against external physical evidence to determine whether interpretations are supported by observable reality. By tracking factual errors, hallucinations, omissions and temporal inconsistencies, WITT provides both a quality benchmark for data users and a preference signal for model training, continuously guiding AI systems toward more accurate and physically grounded understanding. WITT achieves an average factual error rate approximately one-third that of leading general-purpose AI models in autonomous driving scenario understanding tasks. WITT automatically identifies high-value facts and routes them into the most effective learning workflows to maximize model improvement. Rare long-tail scenarios can be returned to WeRide GENESIS, the company’s proprietary general-purpose simulation model, for simulation training and scenario expansion. High-frequency everyday scenarios can support reinforcement learning and workflow optimization. Abnormal or ambiguous data can be directed into review processes to prevent valuable information from being mistakenly discarded as noise.

Ankündigung • Feb 12

WeRide Inc. and Uber Technologies, Inc. Begins First Commercial Robotaxi Service in Downtown Abu Dhabi

WeRide Inc. and Uber Technologies, Inc. have launched the first commercial Robotaxi service in downtown Abu Dhabi - marking the Emirate's first autonomous vehicle (AV) deployment in its city center. With this latest downtown expansion, the WeRide- Uber service now reaches approximately 70% of Abu Dhabi's core areas, with the fleet quadrupling in size since starting operations in December 2024. The newly expanded service operates across Khalifa City, Masdar City, Rabdan, and routes between Corniche Roadand the Sheikh Zayed Grand Mosque, in partnership with the Integrated Transport Centre (ITC). Within the coverage area are major landmarks such as Sheikh Zayed Grand Mosque and popular hotels along Corniche Road, including Grand Hyatt Abu Dhabi Hotel & Residences; InterContinental Abu Dhabi by IHG; Edition Hotel, Abu Dhabi; Emirates Palace Mandarin Oriental Rixos Marina Abu Dhabi; and Radisson Blu Hotel & Resort, Abu Dhabi Corniche. WeRide's Robotaxi GXR at Grand Mosque, downtown Abu Dhabi. Starting February 12, 2026, both companies committed to deploy at least 1,200 Robotaxis across the Middle East as soon as 2027, spanning Abu Dhabi, Dubai, and Riyadh. In November 2025, WeRide and Uber launched the Middle East's firstLevel 4 fully driverless Robotaxi commercial operations in Abu Dhabi, which started with Yas Island. The launch was enabled by WeRide's Robotaxa securing the world's first city-level fully driverless Robotaxi permit outside the US in October 2025. WeRide Robotaxi on Yas Island, Abu Dhabi. WeRide maintains a 4-year first mover advantage in autonomous vehicle deployment in Abu Dhabi, having operated Robotaxis in Abu Dhabi since 2021. In 2023, it became the first company in the UAE to receive a national license covering all types of self-driving vehicles, authorizing autonomous testing and operation on public roads across the country, subject to emirate-level approvals. In December 2024, WeRide and Uber launch their Robotaxi ride-hailing partnership in Abu Dhabi - the largest commercial Robotaxi service outside the UAE by IHG; Edition Hotel, Abu Dhabi; Emirates Palace Mandarin Oriental RixosMar Abu Dhabi; and RadissonBlu Hotel & Resort, Abu Dhabi; riders using the Uber platform and travelling to or from the new areas may be matched with a WeRide Robotaxi GXR through UberX or Uber Comfort, and can also choose to book directly via the dedicated "Autonomous" option on the app. Public commercial operations are now underway with a vehicle specialist on-board, as part of a phased approach towards fully driverless operations.awasul Transport, a leading UAE national transport company, is the main fleet operator for WeRide vehicles on the Uber platform, providing fleet management services. This expansion enhances urban connectivity, improves transport accessibility, and supports the UAE's broader smart mobility vision. Previously, the service covered about half of Abu Dhabi's core area, including Al Reem, Al Maryah, Yas, and Saadiyat, as well as highway routes to and from Zayed International Airport. WeRide now has over 200 Robotaxis in the Middle East. WeRide's Robotxi GXR on Corniche Road, downtown Abu Dhabi. The Sheikh Zayed Grand Mosque was named the top attraction in the Middle East in 2025, and a popular tourist destination on the Uber app. The expanded WeRide- Uber Robotaxi service supports Abu Dhabi's growth as a tourism hub, with hotels across the Emirate receiving 4.8 million guests as of 2024 - a 27% year-on-year increase in international visitors. By connecting this high-demand hotel and tourism corridor with other parts of Abu Dhabi, the service highlights ITC's confidence in the partners' ability to operate safely in complex urban environments. This development highlights WeRide and Uber's rapid progress in the UAE, as they work to scale their Middle East operations to scale their Middle East operations To scale their Middle East operations to thousands of Robotaxi operations to thousands of Robotaxis over the coming years.

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Gesamtverbindlichkeiten	CN¥1.36b
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Daten	Zuletzt aktualisiert (UTC-Zeit)
Unternehmensanalyse	2026/09/11 15:28
Aktienkurs zum Tagesende	2026/09/11 00:00
Gewinne	2026/06/30
Jährliche Einnahmen	2025/12/31

Paket	Daten	Zeitrahmen	Beispiel US-Quelle ^*
Finanzdaten des Unternehmens	10 Jahre	Gewinn- und Verlustrechnung Kapitalflussrechnung Bilanz	SEC-Formular 10-K SEC-Formular 10-Q
Konsensschätzungen der Analysten	+3 Jahre	Finanzielle Vorausschau Kursziele der Analysten	Analysten Research Berichte Blue Matrix
Marktpreise	30 Jahre	Aktienkurse Dividenden, Splits und Aktionen	ICE-Marktdaten SEC-Formular S-1
Eigentümerschaft	10 Jahre	Top-Aktionäre Insiderhandel	SEC-Formular 4 SEC-Formular 13D
Verwaltung	10 Jahre	Das Führungsteam Direktorium	SEC-Formular 10-K SEC-Formular DEF 14A
Wichtige Entwicklungen	10 Jahre	Ankündigungen des Unternehmens	SEC-Formular 8-K

Analyst	Einrichtung
Chen Chi	BNP Paribas
Yawen Tan	BNP Paribas
Ming-Hsun Lee	BofA Global Research

WeRide Balance Sheet Health

Finanzielle Gesundheit Kriterienprüfungen 5/6

Wichtige Informationen

Jüngste Berichte zur Finanzlage

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