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Advanced Snowplow Development and Demonstration Project

Started: January, 1998 Ended: June, 1999 Project ID #429520 Status: Completed


The California Department of Transportation (Caltrans) initiated the Automated Snowplow Program (ASP) to improve the efficiency and safety of snow removal operations. The program’s objective was to apply technologies previously developed under Advanced Vehicle Control and Safety Systems (AVCSS) and Automated Highway Systems (AHS) research to provide assistance to snowplow operators.  The role of the Western Transportation Institute was to assist in demonstration site selection, assist in the development of system requirements, provide methodologies for evaluation of efficiency and safety and technology performance, and collect and analyze data based upon an evaluation plan.


Winter maintenance operations, including snow removal, are subject to increased risk by conditions such as total visual whiteout, low traction on wet or icy pavement, drifting snow and roadways completely covered by snow. Additional hazards are posed by objects buried in or obscured by snow. Furthermore, snowplow operators often lack important visual cues as to their position on the roadway due to accumulations of snow from previous plowing activities. In many of the colder, mountainous states, snowplows that run off the roadway have greater potential for equipment damage and personal injury due to the mountainous terrain in which much of the snow removal operations take place. The opportunity to address these risks with Advanced Vehicle Control Systems (AVCS) technologies was the impetus for the Advanced Snowplow Development and Demonstration project. These AVCS technologies have been incorporated into the United States Department of Transportation’s (USDOT) Intelligent Vehicle Initiative (IVI) program.The main purpose of the Advanced Snowplow Development and Demonstration project was to design, integrate and test AVCS technologies on snowplow maintenance equipment. This phase of the project has attempted to assess potential benefits associated with combining conventional snowplow operations with Intelligent Vehicle (IV) technologies in terms of improved efficiency and safety on rural roadway segments in California and Arizona. A prototype snowplow was equipped with lateral lane indication and forward collision warning systems. The subsequent evaluation attempted to determine the effectiveness of the advanced technology system in terms of safety and operational efficiency, as well as perceived benefits or concerns expressed by the snowplow operators. In addition, the accuracy and reliability of the system was examined. The results of this initial evaluation will first, provide the results necessary to determine the feasibility of AVCS in improving safety and efficiency of snowplow removal and second, help establish a methodology for future AVCS evaluation projects.The demonstration team represented in this phase of the ASP project was made up of technology developers, vehicle operators, research organizations and governmental entities. The California Department of Transportation (Caltrans) and the Arizona Department of Transportation (ADOT) are responsible for snow removal on their State highways and play a key part in the development and deployment of both specialty vehicles and cooperative infrastructure technologies in their States. The University of California – Berkeley, Partners for Advanced Transit and Highways (PATH) program, University of California – Davis, Advanced Highway Maintenance and Construction Technology (AHMCT) program and the Western Transportation Institute (WTI) at Montana State University – Bozeman are the national leaders in research in their respective specialties of vehicle automation technology, specialized maintenance vehicle development and rural transportation research.


Files & Documents

Sponsors & Partners

  • Advanced Highway Maintenance & Construction Technology Sponsor
  • University of California UC Davis Partner

Part of: Safety and Operations

Project Tagged In: snow removal

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