Impact of Automation on Drivers' Performance in Agricultural Semi-Autonomous Vehicles

Area of Science:

• Agricultural Ergonomics
• Human Factors Engineering
• Automation and Control

Background:

• Increasing automation in agricultural vehicles raises concerns about driver mental workload.
• A human factors approach is crucial for understanding the impact of these automated systems.
• Previous research indicates potential negative effects of driving assistant systems on mental workload.

Purpose of the Study:

• To investigate the effects of vehicle steering task automation (VSTA) and implement control and monitoring task automation (ICMTA) on driver performance.
• To assess the relationship between automation levels and drivers' perceived mental workload in a tractor-air seeder system.
• To provide a human factors perspective on the consequences of automation in agricultural machinery.

Main Methods:

• A tractor driving simulator (TDS) was utilized for the study.
• Participants with tractor driving experience performed tasks under varying levels of VSTA and ICMTA.
• Driver performance was measured by reaction time and accuracy of actions.

Main Results:

• Increased automation levels led to decreased driver reaction time.
• Higher automation levels resulted in a reduced number of driver errors.
• Significant correlations were observed between performance metrics and subjective mental workload.

Conclusions:

• Automation in tractor-air seeder systems appears to enhance driver performance by reducing reaction time and errors.
• The findings suggest that increased automation may influence drivers' perceived mental workload.
• A human factors approach is essential for designing and implementing effective agricultural automation.