Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks.
Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficienc…
Full-vehicle crash simulations that account for occupant injury are essential for automobile safety assessment; however, they are computationally intensive and time-consuming. In particular, dash panel deformation plays a key role in transmitting impact loads to an occupant’s low…
This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increas…
Enhancing the safety of passengers by venturing beyond the limits of a human driver is one of the main ideas behind autonomous vehicles. While drifting is mostly witnessed in motorsports as an advanced driving technique, it could provide many possibilities for improving traffic s…
Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level…
External human–machine interfaces (eHMIs) may be useful for communicating the intention of an autonomous vehicle (AV) to road users, but it is questionable whether an eHMI is effective in guiding the actual behavior of road users, as intended by the eHMI. To address this question…