Sensing technology is facilitating the transition of drone delivery services from trial phases to regular daily operations.
Updated
January 8, 2026 6:27 PM
A quadcopter drone with package attached. PHOTO: FREEPIK
A new partnership between Hesai Technology, a LiDAR solutions company and Keeta Drone, an urban delivery platform under Meituan, offers a glimpse into how drone delivery is moving from experimentation to real-world scale.
Under the collaboration, Hesai will supply solid-state LiDAR sensors for Keeta’s next-generation delivery drones. The goal is to make everyday drone deliveries more reliable as they move from trials to routine operations. Keeta Drone operates in a challenging space—low-altitude urban airspace. Its drones deliver food, medicine and emergency supplies across cities such as Beijing, Shanghai, Hong Kong and Dubai. With more than 740,000 deliveries completed across 65 routes, the company has discontinued testing the concept. It is scaling it. For that scale to work, drones must be able to navigate crowded environments filled with buildings, trees, power lines and unpredictable conditions. This is where Hesai’s technology comes in.
Hesai’s solid-state LiDAR is integrated into Keeta's latest long-range delivery drones. LiDAR stands for Light Detection and Ranging. In simple terms, it is a sensing technology that helps machines understand their surroundings by sending out laser pulses and measuring how they bounce back. Unlike GPS, LiDAR does not rely solely on satellites to determine position. Instead, it gives drones a direct sense of their surroundings, helping them spot small but critical obstacles like wires or tree branches.
In a recent demonstration, Keeta Drone completed a nighttime flight using LiDAR-based navigation alone without relying on cameras or satellite positioning. This shows how the technology can support stable operations even when visibility is poor or GPS signals are limited.
The LiDAR system used in these drones is Hesai’s second-generation solid-state model known as FTX. Compared with earlier versions, the sensor offers higher resolution while being smaller and lighter—important considerations for airborne systems where weight and space are limited. The updated design also reduces integration complexity, making it easier to incorporate into commercial drone platforms. Large-scale production of the sensor is expected to begin in 2026.
From Hesai’s perspective, delivery drones are one of several forms robots are expected to take in the coming decades. Industry forecasts suggest robots will increasingly appear in many roles from industrial systems to service applications, with drones becoming a familiar part of urban infrastructure rather than a novelty.
For Keeta Drone, this improves safety and reliability. And for the broader industry, it signals that drone logistics is entering a more mature phase—one defined less by experimentation and more by dependable execution. Taken together, the partnership highlights a practical evolution in drone delivery.
As cities grow more complex, the question is no longer whether drones can fly but whether they can do so reliably, safely and at scale. At its core, this partnership is not about drones or sensors as products. It is about what it takes to make a complex system work quietly in real cities. As drone delivery moves out of pilot zones and into everyday use, reliability matters more than novelty.
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HKU professor apologizes after PhD student’s AI-assisted paper cites fabricated sources.
Updated
January 8, 2026 6:33 PM
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The University of Hong Kong in Pok Fu Lam, Hong Kong Island. PHOTO: ADOBE STOCK
It’s no surprise that artificial intelligence, while remarkably capable, can also go astray—spinning convincing but entirely fabricated narratives. From politics to academia, AI’s “hallucinations” have repeatedly shown how powerful technology can go off-script when left unchecked.
Take Grok-2, for instance. In July 2024, the chatbot misled users about ballot deadlines in several U.S. states, just days after President Joe Biden dropped his re-election bid against former President Donald Trump. A year earlier, a U.S. lawyer found himself in court for relying on ChatGPT to draft a legal brief—only to discover that the AI tool had invented entire cases, citations and judicial opinions. And now, the academic world has its own cautionary tale.
Recently, a journal paper from the Department of Social Work and Social Administration at the University of Hong Kong was found to contain fabricated citations—sources apparently created by AI. The paper, titled “Forty Years of Fertility Transition in Hong Kong,” analyzed the decline in Hong Kong’s fertility rate over the past four decades. Authored by doctoral student Yiming Bai, along with Yip Siu-fai, Vice Dean of the Faculty of Social Sciences and other university officials, the study identified falling marriage rates as a key driver behind the city’s shrinking birth rate. The authors recommended structural reforms to make Hong Kong’s social and work environment more family-friendly.
But the credibility of the paper came into question when inconsistencies surfaced among its references. Out of 61 cited works, some included DOI (Digital Object Identifier) links that led to dead ends, displaying “DOI Not Found.” Others claimed to originate from academic journals, yet searches yielded no such publications.
Speaking to HK01, Yip acknowledged that his student had used AI tools to organize the citations but failed to verify the accuracy of the generated references. “As the corresponding author, I bear responsibility”, Yip said, apologizing for the damage caused to the University of Hong Kong and the journal’s reputation. He clarified that the paper itself had undergone two rounds of verification and that its content was not fabricated—only the citations had been mishandled.
Yip has since contacted the journal’s editor, who accepted his explanation and agreed to re-upload a corrected version in the coming days. A formal notice addressing the issue will also be released. Yip said he would personally review each citation “piece by piece” to ensure no errors remain.
As for the student involved, Yip described her as a diligent and high-performing researcher who made an honest mistake in her first attempt at using AI for academic assistance. Rather than penalize her, Yip chose a more constructive approach, urging her to take a course on how to use AI tools responsibly in academic research.
Ultimately, in an age where generative AI can produce everything from essays to legal arguments, there are two lessons to take away from this episode. First, AI is a powerful assistant, but only that. The final judgment must always rest with us. No matter how seamless the output seems, cross-checking and verifying information remain essential. Second, as AI becomes integral to academic and professional life, institutions must equip students and employees with the skills to use it responsibly. Training and mentorship are no longer optional; they’re the foundation for using AI to enhance, not undermine, human work.
Because in this age of intelligent machines, staying relevant isn’t about replacing human judgment with AI, it’s about learning how to work alongside it.