When people think of autonomous vehicles, they often picture self-driving cars navigating city streets. However, the same Advanced Driver Assistance Systems (ADAS) and autonomy levels that are shaping the future of automobiles are also revolutionizing off-highway sectors like agriculture.
Modern farming equipment now integrates GNSS-guided steering, sensor-based obstacle detection, and AI-driven automation enabling machines to operate with minimal human intervention. Thus, reducing human workload while optimizing performance.
At the heart of this transformation lies the concept of autonomy levels, which define the extent to which machines can operate independently. These levels, ranging from basic driver assistance to fully autonomous operation, dictate how Artificial Intelligence (AI) interacts with the equipment’s safety and control systems. As AI becomes more sophisticated, its integration into the safety layer of agricultural machinery ensures not only efficiency but also reliability in unpredictable farming environments.
Although the levels of autonomy, defined by the SAE (Society of Automotive Engineers) J3016 standard, were originally designed for on-road vehicles, many agricultural machinery manufacturers and researchers have adapted these levels to define autonomy in precision farming and autonomous tractors. While the operational context differs, the fundamental principles of automation, perception, and decision-making remain the same.
The 6 Levels of Autonomy
Level 0 – Manual Operation
At Level 0, the vehicle or machine operates entirely under human control. There are no automated systems or assistance features. The operator is responsible for all tasks, including steering, speed control, and obstacle avoidance. Any safety features, such as emergency stops or warning lights, are purely passive and do not assist in automation.
Level 1 – Driver Assistance
Level 1 introduces basic driver assistance systems, where a specific function, like steering or speed control, is automated, but not both simultaneously. For example, a machine might have cruise control or GNSS-assisted steering, but the operator still maintains full control over other aspects of operation, such as speed or navigation. These systems support the operator but do not take over significant control of the machine.
Examples of technologies involved:
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Cruise control
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GNSS-assisted steering systems
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Basic driver assistance systems
Level 2 – Partial Automation
At Level 2, the machine can automate multiple tasks simultaneously, such as steering and speed control, but the operator remains engaged and must monitor the system. For instance, a tractor with auto-steering and speed regulation can follow a predefined path autonomously, but the operator must be ready to intervene at any time. The equipment provides a higher level of automation but still requires human oversight and control.
Examples of technologies involved:
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Auto-steering systems
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Speed regulation systems
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GNSS-based navigation systems
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Radar and Lidar for obstacle detection
Level 3 – Conditional Automation
In Level 3, the machine can handle certain tasks autonomously without human intervention, but only under specific conditions. The machine's automated systems can perform all driving functions, such as steering, braking, and acceleration, in controlled environments like a field. However, the operator must be available to take control when the system encounters situations it cannot handle, such as unexpected obstacles. The equipment may alert the operator to intervene if necessary.
Examples of technologies involved:
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Path planning algorithms
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Automated attachment control systems
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Advanced vision systems for obstacle detection and recognition
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Machine learning to adapt to different environments
Level 4 – High Automation
At Level 4, the machine operates fully autonomously within a defined environment or set of conditions, such as a specific field or worksite. It can handle all tasks without human intervention, including complex decision-making like obstacle avoidance or path planning. While an operator is not needed during regular operations, human control might still be required if the machine encounters an environment beyond its operational limits (e.g., crossing boundaries of the field).
Examples of technologies involved:
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Sensor fusion systems
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AI-based decision-making algorithms
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Obstacle avoidance technologies
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Path optimization algorithms
Level 5 – Full Automation
Level 5 represents complete autonomy, where the machine operates independently in all environments and conditions. The vehicle or equipment performs all tasks—ranging from driving to decision-making—without human involvement. No operator is needed, as the system can manage all navigation, safety, and task execution. This level of autonomy is fully independent and can operate without restrictions or human oversight, ensuring maximum efficiency and productivity.
Examples of technologies involved:
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Full sensor suite (e.g., 360-degree cameras, Lidar, radar)
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AI-driven decision-making with deep learning
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Advanced robotics for task execution
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Autonomous navigation systems
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Comprehensive real-time data processing
Why ISO 18497 Matters ?
ISO 18497 is an international standard that sets safety requirements for highly automated agricultural machinery, ensuring their reliable and safe operation in farming environments. It provides guidelines for sensor-based systems, fail-safe mechanisms, and functional safety to prevent accidents and enhance operational efficiency. Compliance with this standard is crucial for manufacturers developing advanced autonomous farming solutions.
One critical component of this safety framework is the Sensor and Perception Systems including technologies like cameras, Lidar, radar, ultrasonic sensors, and GNSS that can be used in obstacle detection and navigation. The ISO 18497 standard emphasizes the redundancy and reliability of sensors, ensuring that if one fails, others can compensate. It also ensures that the perception systems can detect dynamic obstacles and adapt to changing environments to maintain safe navigation.
Perception sensor mounted on a tractor
Dilepix – Your Innovation Partner
At Dilepix, we specialize in Artificial Intelligence (AI) and Computer Vision solutions shaped for autonomous off-highway vehicles, including agricultural robotics. Our expertise in perception systems ensures that autonomous tractors and machinery can navigate complex environments with reliability and precision.
We support manufacturers and research institutions by providing services that include:
- AI consultancy to optimize perception and navigation systems
- Custom software development for Advanced Driver Assistance Systems (ADAS)
- Hardware selection and integration of Lidar, radar, and vision sensors
- Performance validation and compliance with ISO 18497 safety standards
With Dilepix, you gain a long-term innovation partner dedicated to enhancing safety, efficiency, and productivity in autonomous farming.
