Lidar Applications Beyond Automotive: How Physical AI Is Transforming Defense 

For years, lidar has been closely associated with autonomous vehicles. It became synonymous with helping cars detect obstacles, navigate complex environments, and improve safety. 

Today, however, fast-growing opportunities for lidar arise beyond the automotive industry. One of those applications is in the defense industry.  

As defense organizations around the world invest in autonomous systems, robotics, and intelligent sensing, Physical AI is becoming an increasingly important capability. Whether supporting unmanned vehicles, counter-drone operations, or mission planning, modern defense systems depend on one fundamental capability: the ability to accurately perceive and understand the physical world in real time. 

Lidar is helping enable that transformation. 

Defense Is Becoming a Physical AI Problem 

Modern defense operations are increasingly relying on autonomous and semi-autonomous systems to perform missions that are too dangerous, repetitive, or complex for humans alone. 

Unmanned ground vehicles (UGVs), autonomous aerial systems (UAVs), robotic reconnaissance platforms, and intelligent perimeter security systems are becoming more common across defense applications. These systems require far more than navigation; they need to continuously perceive, interpret, and respond to dynamic environments.  This is the essence of Physical AI. 

Unlike traditional AI, which processes digital information, Physical AI enables machines to understand and interact with the physical world. Before an autonomous system can make a decision, avoid an obstacle, or complete a mission, it must first perceive what is happening around it. That makes perception foundational. 

Detecting Difficult-to-See Obstacles: Power Lines 

As autonomous systems take on a greater role in defense operations, they must be able to detect more than large, obvious obstacles. Some of the most difficult hazards to perceive can also be among the most consequential. 

Power lines are a good example. Their thin profile can make them difficult for conventional imaging systems to identify, particularly when an aircraft is operating at low altitude, moving against a complex background, or navigating in challenging lighting conditions. Yet for unmanned aerial systems and other autonomous platforms, failing to detect a power line early enough can pose a serious risk to both the vehicle and the mission. 

This is where high-resolution 3D perception can provide an important advantage. Lidar directly measures the surrounding environment rather than relying solely on visual appearance, color, or contrast. Research has demonstrated the use of lidar to detect and model power lines in three dimensions, providing autonomous systems with additional spatial information that can help identify thin obstacles and understand where they are positioned within the environment. 

For defense applications, that capability can become especially important as unmanned aircraft operate closer to the ground and navigate increasingly complex terrain. Detecting a power line is only part of the challenge; the system must detect it early enough to understand the obstacle, determine its position, and safely adjust its path. 

As Physical AI continues to evolve, perception systems will need to recognize not only vehicles, buildings, and terrain, but also smaller environmental hazards that can affect how safely an autonomous system completes its mission. Power-line detection is one example of how advanced lidar perception can give autonomous platforms a more complete understanding of the environments in which they operate. 

Enabling Autonomous Military Vehicles 

Another significant application for lidar is supporting autonomous navigation across unmanned military platforms. 

Autonomous ground vehicles must navigate rough terrain, identify obstacles, select traversable routes, and safely maneuver without relying solely on human operators. Similarly, unmanned aerial systems benefit from accurate three-dimensional perception for terrain following, collision avoidance, precision landing, and low-altitude navigation. Maritime autonomous platforms are also increasingly incorporating advanced sensing to improve vessel detection and obstacle avoidance.  

As autonomy expands, reliable perception becomes a key enabler of mission success. 

Countering Emerging Drone Threats 

The rapid evolution of unmanned aerial systems has introduced new challenges for defense organizations worldwide.  

Counter-UAS operations increasingly depend on multiple sensing technologies working together to detect, classify, and track small aerial threats. Rather than relying on a single sensor, many modern approaches use layered sensing architectures that combine radar, cameras, AI, and other technologies to build a more complete understanding of the environment. Traditional defense systems have long relied on radar to detect airborne threats. However, drones present a different challenge. Their small size, relatively slow speed, and high maneuverability can make it difficult for radar alone to distinguish them from innocuous objects such as birds or even environmental movement like swaying branches. Missiles and other conventional airborne threats typically exhibit characteristics that make them easier for radar systems to detect and classify. Small drones challenge those traditional assumptions, increasing the need for complementary sensing technologies that can provide additional information to help distinguish a potential threat from a bird or other benign object. 

High-resolution 3D perception can complement these systems by improving spatial awareness and helping distinguish potential threats in complex environments. 

As drone technology continues to evolve, so too must the perception systems responsible for identifying them.  

Why Software-Defined Perception Matters 

No two defense missions are the same. A reconnaissance drone flying through an urban environment has different sensing requirements than an autonomous ground vehicle traversing rough terrain or a fixed installation monitoring a secure perimeter. Traditional sensing systems are often optimized for a single application, while software-defined perception changes that equation. 

Rather than requiring entirely new hardware for each mission, software-defined sensing allows perception performance to be configured and optimized for different operating environments. This flexibility enables organizations to adapt sensing capabilities as mission requirements evolve without redesigning the underlying hardware. 

As Physical AI expands across defense, adaptable perception will become increasingly valuable. Autonomous systems need to operate across vastly different missions and environments, making a one-size-fits-all approach to sensing increasingly limiting. Software-defined lidar helps close that gap by allowing perception capabilities to be tailored through software to the needs of a specific mission, without requiring an entirely new hardware platform. 

At AEye, we believe software-defined, long-range perception will play an increasingly important role in enabling the next generation of Physical AI, helping autonomous systems across transportation, infrastructure, robotics, and defense better perceive, understand, and safely interact with the world around them. Learn more about our software–defined lidar here.