Advances on risky driver behaviour detection in road vehicles: a systematic literature review
Year: 2026
Type: review
Study design: Systematic Review
Abstract: The automotive sector is undergoing continuous technological evolution driven by the demand for sustainable and safe vehicles. Among the main factors influencing safety, driver behaviour has been identified as a critical contributor to road crashes. This systematic review explores recent innovations in detecting risky driver behaviours, addressing six research questions: the most relevant datasets used for algorithm development and evaluation; system architectures and methodologies for anomaly detection; the most studied driver behaviours and related environmental, human, and mechanical factors; advances in machine learning, deep learning, and statistical methods; performance metrics and validation approaches; and the role of embedded technologies and sensors in practical applications. The review included 93 peer-reviewed articles published between 2020 and 2024, sourced from ACM, IEEE, ScienceDirect, and Scopus. Exclusion criteria were duplicates, non-open access, retracted works, and studies unrelated to outlier detection or driver behaviour. The Parsifal tool was used to support systematic data processing. Results highlight the most frequently used datasets, proposed models, and their performance in detecting driver behaviours, as well as the influence of contextual factors such as traffic rules, road conditions, and sensor limitations. Despite advances, real-world integration remains challenging, requiring further research and development. This review aims to guide researchers in understanding the current state of anomaly detection in driving contexts and to emphasize the need for broader collaboration to create effective, deployable solutions that enhance road safety worldwide.
Source: Artificial Intelligence Review
Authors: Luís Ferreira, António Valente, Paulo Salgado, José Boaventura
Institutions: University of Trás-os-Montes and Alto Douro, INESC TEC
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
OpenAlex ID: W7126098138