In today’s dynamic environments, where threats evolve in real time, people move unpredictably, and automation demands split-second decisions, understanding motion is everything.

At Quanergy, we believe that motion is not noise, it’s intelligence. Our mission is to transform motion into intelligence to empower decision makers to extract clear, actionable signals from our chaotic and dynamic physical environments.

The Motion Problem: A Universal Challenge

From protecting data centers to managing airport congestion or guiding autonomous vehicles, the core issue is the same: motion is difficult to track, interpret, and respond to, especially when traditional technologies are used.

Physical Security – When Every Swaying Tree Looks Like a Threat

Perimeter security is a high-stakes game. Whether it’s a data center, utility substation, or rail corridor, intrusions can cost millions and threaten business continuity.

Despite expansive investments in video analytics cameras don’t know what matters.

They react to any motion, whether it’s a dog wandering past, a gust of wind rustling tree branches, or cars driving by. This results in:

• False alarms that waste resources
• Desensitized operators who stop reacting
• Missed threats that slip past

The fundamental problem is the passive 2D technology used, that is not designed to accurately capture motion in a 3D world, making it difficult to precisely locate and track intruders.

To track a moving person across a site, operators must stitch together multiple screens, with shadows, poor lighting and occlusions, trying to anticipate where someone might go next, often too late to act.

Radar has a different problem. It’s typically optimized for either low-speed motion (people) or high-speed motion (vehicles), but not both. In environments like utilities, this becomes a dangerous limitation.

These limitations not only impact the security of buildings and the safety of people, but also contribute to escalating theft and pilferage. According to Wired, “Metal Prices Are Soaring. So Is Metal Theft.” Metal theft is labeled a “global epidemic,” with copper repeatedly stolen, even from rail infrastructure, because legacy sensors just don’t catch thieves moving among vehicles and trains.

Smart Spaces – Airports Are Rivers of People. Cameras Can’t See the Flow.

Airports are among the most complex motion environments in the world. At any given time, thousands of passengers are walking, pausing, turning around, entering queues, or clustering near gates and around columns, counters and other occlusions.

And airports operators need to precisely answer key questions:

• Where are bottlenecks forming?
• How long are passengers waiting at security?
• How many operators do we need to dispatch?

Cameras can capture images but cannot effectively track the motion of passengers across large terminals. Due to their limited coverage and lack of meshing capabilities, hundreds if not thousands of sensors would be needed, making the cost of the required networking, cabling and installation infrastructure unaffordable.

Cameras’ lack of scalable intelligence impacts Airports’ ability to:

• Reduce queue lengths and wait times
• Optimize staffing decisions
• Increase retail and concession revenue
• Offer efficient security screening

And because they collect identifiable imagery, cameras introduce privacy and regulatory challenges, especially in international terminals. This often results in limited deployment and underutilized data.

Gartner predicts that “80% of 3D imaging sensors for smart spaces will use LiDAR or mm Wave radar as the primary vision imaging sensor by 2030.”

Transportation – Motion Overload

In rail and metro stations, motion sensing becomes even more difficult. Imagine trying to answer a seemingly simple question: “How many people just boarded that train?”

Now add these layers of complexity:

• Trains are large, fast, and reflective.
• People are small and slow-moving.
• There are metal surfaces, glass doors, and multiple entry points.

Cameras struggle with this environment. Reflections from train windows distort images. Occlusions make it hard to track individuals. Lighting conditions vary wildly between platforms and tunnels. And again, privacy concerns limit where and how cameras can be used.

Industrial Automation – The AGV’s Dilemma – What’s in My Path?

In the world of automation, especially with autonomous guided vehicles (AGVs), real-time perception isn’t a luxury; it’s mission critical.

Warehouses, manufacturing plants, and fulfillment centers present AGVs with constant motion and variation:

• A pallet may need to be picked up and transported.
• A worker may walk into the AGV’s path.
• A hanging load might protrude from a shelf, threatening a collision.

With 2D LiDAR or cameras, the AGV may detect something, but not what it is, and without that context, it can’t decide how to act.

The Consequences: Cost, Chaos, and Compromise

When legacy systems can’t reliably extract intelligence from motion, the results are measurable:

• False alarms waste security team time and reduce trust in alerts.
• Undetected intrusions lead to theft, vandalism, and safety risks.
• Overcrowded terminals frustrate passengers and reduce airport revenue.
• Inefficient AGV operation results in slowdowns, accidents.

Across all these environments, the biggest technical gap is object persistence over time, the ability to know that the person seen in Zone A is the same person in Zone B, despite occlusion, crowding, or changing context. This requires:

• High-accuracy clustering to define what each object is.
• Robust tracking to follow objects through 3D space.
• Precise association to maintain identity across time and transitions.

The Solution: Quanergy 3D LiDAR – Transforming Motion into Intelligence

Our mission at Quanergy is to design 3D LiDAR-based solutions to address the complexity inherent in dynamic physical environments:

• Real-time 3D object classification and tracking – across people, vehicles, trains and pallets.
• Event-to-action for security – including loitering, trespassing, tailgating.
• Full journey object ID persistence – built on high clustering, tracking, and association accuracy.
• Mixed-object tracking – in environments like train platforms, where hundreds of people interact with massive, moving vehicles.
• Intelligent AGV operations – help detect and differentiate pallets, humans, and hanging loads, while limiting sensor cross-talk and motion blur.

It’s not just motion detection. It’s motion interpretation.

Final Word: Transforming Motion is a $100B Market Opportunity.

The scope of the problem and the size of the opportunity is over $100B.