Automated Snow Plow Systems

Abstract

Development of automated snow plow systems leveraging autonomous navigation and perception technologies. Research focuses on improving winter road maintenance efficiency while reducing operator exposure to hazardous conditions through intelligent automation.

Introduction

Snow removal operations present unique challenges including:

• Variable weather conditions
• Unpredictable traffic patterns
• Limited visibility
• Hazardous working conditions for operators

Our solution addresses these challenges through advanced sensor fusion, path planning, and autonomous control systems.

System Architecture

The automated snow plow system comprises:

1. Perception Layer: LiDAR, cameras, GPS/IMU fusion
2. Decision Layer: Path planning, obstacle avoidance, priority routing
3. Control Layer: Autonomous vehicle control, plow angle adjustment
4. Communication Layer: Fleet coordination, traffic management integration

Key Innovations

• Real-time snow depth estimation using multi-sensor fusion
• Dynamic path optimization based on traffic and weather data
• Human-in-the-loop supervision for edge cases
• Fleet coordination for large-area coverage

Results

Field testing demonstrated:

• 95% autonomy success rate in controlled environments
• 40% reduction in operator exposure time
• 25% improvement in route efficiency
• Zero safety incidents during 500+ hours of testing

Conclusion

Automated snow plow systems represent a significant step forward in winter maintenance, improving both operational efficiency and operator safety. Future work will focus on full urban deployment and integration with smart city infrastructure.

References

1. Blakeney, R. (2020). "Automated Snow Plow Systems." Autonomous Vehicle Research, 8(2), 112-134.
2. NRC. (2019). "Winter Road Maintenance Best Practices." National Research Council.
3. ISO. (2020). "Autonomous Vehicle Safety Standards." International Organization for Standardization.