Preventing Rear-end and Side Crashes of Heavy-Duty Tractor Trailer Combinations with Smart Sensors and Vision Systems
Inconsistencies in inspection outcomes, whether performed by human inspectors or AI systems, pose significant safety risks for heavy-duty vehicles (HDVs).
Human inspectors struggle with vast vehicle states and fault modes, while AI systems face challenges due to insufficient or sparse historical data.
These challenges lead to critical safety issues, such as brake systems account for 29% of all HDV crashes, and in 2021, 15.6% of HDVs involved in crashes had brake inspection violations.
Human inspectors, with their tacit knowledge, uniquely determine where to focus, when to stop collecting information, and how to use the collected information to make decisions.
Therefore, unveiling human decision-making factors is key to improving predictive inspections and prioritizing high-risk components.
So far, we collected survey data to analyze which factors influence humans’ decision making while inspecting HDVs.
For the next step, we will apply causal analysis using knowledge graphs and cutting-edge causal inference methods enabling AI to assist humans in making better decisions.
Researchers
