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.

Abstract
This review examines the vehicle communication systems, its evaluation measures, security concern and impact of contemporary technology. By making electronic searches through different databases, 20 articles were identified to include in the study. Findings have demonstrated that more sophisticated protocols are being implemented, e.g., FlexRay and Dedicated Short-Range Communication (DSRC), though older protocols, e.g., Controller Area Network (CAN) and Local Interconnect Network (LIN), remain widespread. Additionally, the use of Ethernet-based systems in automotive communications is increasing. However, many of these protocols have substantial vulnerabilities, which pose significant security challenges. The findings suggest adopting enhanced communication and security measures supported by Artificial Intelligence (AI) and Machine Learning (ML) for future vehicles. Overall, this work systematically evaluates in-vehicle communication protocols and proposes methods for addressing contemporary security challenges in the automotive industry.