Wheel (mis)alignment is one of the factors influencing vehicle safety, tire wear, and energy efficiency. While alignment procedures are well established in automotive workshops, recent advances in sensing, connectivity, and data-driven methods have led to renewed academic interest. This goes against existing research, which remains fragmented around vehicle types and methodologies. This study conducts a scoping review of wheel alignment monitoring and detection methods, with a focus on passenger vehicles. Guided by PRISMA-ScR, 453 studies were identified, of which 386 were excluded and 13 were duplicates and thus removed, resulting in a small number remaining for thematic analysis. Three dominant methodological approaches emerged: (i) traditional measurement methods, (ii) sensor-based vehicle dynamics analysis, and (iii) data-driven methods employing machine learning and vehicle telemetry. The findings revealed limited research on vehicle applications, especially for intelligent, integrated, and scalable alignment technologies and real-time, in-service monitoring applications. Other challenges included data quality, calibration, and cost-effectiveness. Therefore, the development of an integrated real-time wheel misalignment detection and reporting framework grounded in systems engineering and enterprise architecture principles is proposed.
Mashigo et al. (Sun,) studied this question.