SmarTxT: A Natural Language Processing Approach for Efficient Vehicle Defect Investigation

Abstract

The investigation of vehicle defects, which is generally led by the National Highway Traffic Safety Administration (NHTSA) in the U.S., is critical to the continued trust of the general public in the safety of vehicles. NHTSA routinely receives millions of reports of potential defects, complaints, recalls, and manufacturer communications, which may provide evidence of a new vehicle defect. However, the large quantity and text-based communication make efficiently identifying defect trends difficult for analysts. To accelerate the investigation of defect reports, we introduce a natural language processing (NLP) application that identifies key topics and similar defect reports to assist analysts and investigators. Further, our application is built to provide users with a web interface for interacting with the NLP models. The integration of NLP with current NHTSA datasets provides a method for quickly identifying defect trends in large text-based datasets. To demonstrate the effectiveness of our method, we apply our approach to two publicly available NHTSA datasets, namely the Technical Service Bulletins and Recalls Dataset.

Analysis of Language-Model-Powered Chatbots for Query Resolution in PDF-Based Automotive Manuals

“…Furthermore, extending beyond the realm of automotive services, another pertinent area for NLP is in the field of vehicular defect investigation and analysis, as proposed in [16]. The National Highway Traffic Safety Administration (NHTSA) in the USA stands at the forefront of this domain, regularly receiving an overwhelming influx of reports related to potential defects, recalls, and manufacturer communications.…”

“…With the continuous evolution of conventional vehicles (in opposition to the expected new generation of driverless electric vehicles), which has incorporated increasingly advanced systems and diversified functionalities, the efficient dissemination of information emerges as a pressing challenge [14,15]. Technical manuals represent the main repositories of knowledge about the general operation of the vehicles, guiding users in various tasks, from operation to maintenance and troubleshooting of vehicles [16,17]. However, traditional methods of disseminating information often fail to meet the varying needs of users, resulting in inefficiencies and difficulties in accessing relevant information [5].…”