The Autonomous Robotic Logic Operator, known as ‘ARLO,’ is our autonomy stack that brings together perception, artificial intelligence, real-time software, safety systems and machine integration to turn environmental information into controlled autonomous movement.
Start the Journey
Travel along our technological road and learn what goes into ARLO.
ARLO can be categorised into a four step system, defining the core autonomy loop
ARLO Sees
Overview
Understanding the world around the machine
Before ARLO can decide what to do, it first needs to understand where it is and what is happening around it.
ARLO combines AI vision, AI-enhanced 4D radar, positioning and multiple sensing technologies to continuously build a picture of the operating environment.
Depending on the application, this can include detecting and tracking people, vehicles, machinery, infrastructure, route boundaries, obstacles, operating zones and unexpected hazards.
He’s not simply asking “Is something there?”
He’s building the information needed to understand:
- What is it?
- Where is it?
- How is it moving?
- Does it affect what happens next?
AI-enhanced 4D radar
Seeing movement in another dimension
AI-enhanced 4D radar allows ARLO to perceive the surroundings using radio waves.
Radar can detect objects and calculate their relative movement, providing useful environmental information even in conditions such as rain, snow or fog where purely visual sensors may be more limited. ARLO also applies AI techniques to radar data to improve object detection, tracking and classification.
Radar tells ARLO what is there. AI helps him understand what that information means.
Identify objects and obstacles within the surrounding environment.
AI vision
Giving ARLO another way to see
ARLO’s artificial intelligence systems extend beyond radar. HD cameras work with AI-powered image processing to recognise and classify nearby objects to help him interpret the visual environment.
AI models identify objects within imagery and distinguish between different classes, helping ARLO to determine what he is looking at.
Positioning
Knowing what surrounds you is only half the picture
ARLO also has to understand where the machine itself is operating, which is why we developed CAVGPS to provide accurate positioning information for autonomous systems.
Establish position.
CAVSense
No single sensor has to understand the entire world alone
CAVSense combines the information from different sources to produce a richer understanding of what surrounds the machine.
Identifies objects within the machine’s path.
ARLO doesn’t just detect the world.
It builds an understanding of it.
ARLO Thinks
Overview
Turning perception into real-time decisions
Everything ARLO sees becomes information that can be processed. ARLO continuously has to answer the questions at the heart of autonomous operation:
From those answers he can determine route, direction, speed, stopping position, obstacle avoidance, junction behaviour and mission progression.
“Different missions need different behaviours”
- Where am I?
- What is around me?
- Where am I going?
- What is likely to happen next?
- What should the machine do?
CAVACS
Turning intelligence into autonomous behaviour
CAVACS is EVIE’s autonomous control system, using localisation, mapped route data and decision-making logic to determine how the machine should move through its environment.
It sits between ARLO’s understanding of the world and the mission-specific software that defines where and how the machine operates.
CAVRoutes
Repeatable routes
CAVRoutes supports applications where a machine follows a defined circular route repeatedly.
The current technology was developed for applications such as airport transportation and can incorporate junctions, stops and repeatable operating paths.
CAVP2P
Point to point movement
CAVP2P is designed around movement between defined locations rather than a continuous loop.
That makes it suitable for operations where machines repeatedly move between operational points, such as material handling or depot movements.
CAVFarm
Autonomous field operations
CAVFarm was developed specifically for agricultural applications.
The system can generate an efficient route across a field after its perimeter is defined, supporting autonomous operations such as ploughing, mowing, seeding and spraying.
Collision avoidance
Deciding what happens when the unexpected appears
Perception only creates value when it changes behaviour.
ARLO has the ability to detect obstacles and either stop the machine or determine an alternative route around them within defined operating rules.
Knowing where to go
Autonomous operation depends on more than following GPS coordinates. Routes, operating areas, junctions, stopping locations and other site information become part of the machine’s operational understanding.
Watchdog Checks
Overview
Intelligence decides. Safety determines what is allowed
Every safety-critical command generated by ARLO can be independently monitored and validated by Watchdog before being passed to the vehicle.
Operating limits
Every movement stays inside defined boundaries
If the requested action falls outside an approved operating condition, Watchdog can reject the command or initiate the appropriate safe response.
This creates a deliberate separation between autonomous intelligence and safety-critical vehicle control.
Watchdog can apply predefined vehicle and operational safety rules including:
Safety is not one final check.
Machine Monitoring
Safety-critical systems can be continuously monitored.
Collision Avoidance
Detected obstacles can change autonomous behaviour.
Geofencing
Defined operating boundaries can be monitored.
Human Oversight
Where a situation requires additional context, ARLO can seek guidance rather than simply handing direct control to an operator.
ARLO Acts
Overview
Turning intelligence into movement
Once a requested action has passed through the safety architecture, it has to become a physical machine command.
Depending on the platform, this can include:
CAVDaq
Connecting autonomy to the physical machine
CAVDaq is our flexible data-acquisition hardware.
It can connect to a broad range of external systems including sensors, GPS equipment, 4G and 5G telemetry and CAN-bus devices, bringing machine information into the autonomous architecture.
Machine control
Autonomy and propulsion are separate decisions
The machine does not need to understand autonomy. ARLO gives it the instructions it needs to move.
ARLO decides how to move. The platform determines what makes it move, whether it be:
The Technology Around Autonomy
Beyond seeing, thinking and acting, our wider technology ecosystem connects machines, protects data, supports operators and helps autonomous systems develop and scale.
Connected Autonomy
Overview
Autonomy does not operate in isolation
Machines can be connected to our wider operational infrastructure for monitoring, telemetry, fleet management and communication.
CAVCon
Connecting the machine
CAVCon provides wireless communication between machines and our wider infrastructure.
The existing system allows telemetry to be received from a machine while information, messages, routing changes and control data can also be sent back to it.
CAVCloud
The secure data layer behind connected autonomy
CAVCloud is EVIE’s data platform for securely exchanging information between autonomous machines and monitoring systems.
Working with CAVCon on the machine, it sends data to an online server where it can be accessed by fleet-management systems and used to send instructions back to connected vehicles.
CAVTrak
Managing the fleet
CAVTrak is our fleet-management system.
It monitors the location and status of multiple machines and uses two-way 5G communication to support routing and operating changes in real time.
Cyber security
Protecting the connection
Once autonomous machines become connected machines, communications have to be protected.
Our current architecture uses AES-256 encryption to protect telemetry exchanged between autonomous machines and fleet-management infrastructure.
Protects transmitted telemetry.
Human oversight
Autonomous, with people still in the loop
Instead of necessarily handing direct control to a remote operator, predefined options can be presented so human judgement provides context while the machine continues to execute the resulting behaviour autonomously.
Built Differently
Purpose-built for real-time autonomy
Our hardware/software stack is built around embedded computing and an in-house development environment rather than requiring an unnecessarily large general-purpose computing platform.
Real-Time
Designed for rapid perception and autonomous decision-making.
Efficient Computing
The architecture is designed to operate using compact embedded processing.
Multi-Threaded
CAVLab’s architecture makes extensive use of parallel processing capability.
Edge Processing
AI workloads can be processed close to the machine rather than depending solely on remote infrastructure.
Development & Validation
CAVLab
The software foundation
CAVLab is our in-house development environment and sits at the foundation of much of our autonomous software technology.
It uses our own object-based graphical programming approach for developing real-time autonomous applications. Complete machine simulations can be run in the lab before the same source code is deployed onto the physical machine.
The current system also supports modifying software while the machine is operating autonomously, giving engineers a much faster development and testing cycle.
Build virtually. Prove physically.
Develop and test autonomous behaviour virtually before deployment.
CAVRace
High-speed autonomous development
CAVRace was developed to allow high-speed testing on race tracks in a controlled environment.
The software calculates the racing line, enabling autonomous vehicles to be tested at increased speeds while engineers assess vehicle dynamics through corners and other demanding parts of a circuit.
Every movement is a calculation made real
Autonomy is not one technology. It is a system of technologies continuously working together.
Frequently Asked Questions
Find clear answers to common questions about how EVIE’s technology stack sees, thinks, connects, protects and turns decisions into autonomous movement.
ARLO acts as the intelligent operator by receiving information from the machine’s/asset’s sensors, understanding the surroundings and deciding how the task should be completed. It then sends commands to the machine’s steering, braking, acceleration, hydraulic or other control systems.
Watchdog operates independently as the safety supervisor by continually monitoring ARLO, the machine/asset and the surrounding environment. If anything moves outside the permitted operating or safety limits, Watchdog can override the command, slow the machine, stop it or place it into a safe state.
ARLO combines information from AI cameras, 4D radar and the machine’s own sensors. Depending on the application, this can also include GNSS or RTK positioning, inertial measurement, wheel-speed data, encoders and other task-specific sensors. This information is processed through sensor fusion, creating a reliable, real-time understanding of the machine’s position, surrounding objects, people, vehicles, terrain and potential hazards.
AI-enhanced 4D radar is specifically focused on detecting and tracking physical objects. It measures their distance, direction, height and velocity, including in darkness, dust, fog, rain and other difficult conditions. EVIE’s wider AI systems use this radar information alongside cameras and other sensors to understand the complete situation. They determine where the machine is, interpret what is happening, plan the safest route and control the machine’s actions. The key difference: 4D radar provides critical perception data; ARLO turns that data into intelligent decisions and movement.
EVIE uses multiple positioning methods rather than relying on a single source. These can include GNSS or high-accuracy RTK positioning, inertial sensors, vehicle odometry, digital maps and visual or radar-based localisation. ARLO compares this information with the assigned route, working area or task plan. It continually calculates the machine’s position and adjusts its route as it moves.
Where satellite positioning is unreliable or unavailable, EVIE’s localisation technology can use the machine’s movement and surrounding features to maintain an accurate understanding of its location.
ARLO identifies the object, assesses its position and movement, and determines the level of risk. Depending on the situation, it can reduce speed, maintain a safe distance, plan an alternative route or bring the machine to a controlled stop.
Watchdog independently checks that ARLO’s response remains safe. If the hazard cannot be resolved confidently, the system defaults to a safe state and can request assistance from a remote operator or supervisor.
Safety and security are built into the complete system architecture. ARLO processes critical information directly on the machine, reducing latency and removing the need to depend on a continuous cloud connection. Watchdog independently supervises the machine’s behaviour, while defined operating limits, system-health monitoring, access controls, secure communications, controlled software updates and event logging help protect the system. If a fault, communication loss or abnormal condition is detected, the machine can automatically slow down, stop or enter a predefined safe state.
Yes, EVIE’s technology is designed to be machine, powertrain and sector agnostic. ARLO can be integrated with electric, combustion, hybrid and hydraulic platforms, including both new machines and existing fleets. The core autonomy and safety technology remains consistent, while the sensors, controls and operating software are configured for each machine, task and environment. This allows customers to begin with one application and then scale the technology across additional vehicles, locations and operations.