Amil Roohani Dar

Predicting brake light activation in semi-autonomous vehicles (SAVs) is critical for improving road safety and optimising adaptive cruise control (ACC) systems. Despite the remarkable performance of deep learning-based object detectors such as YOLOv8, their inherent opacity in decision-making processes limits transparency, interpretability, and user trust factors essential for the deployment of safety-critical systems. This study introduces a novel, explainable framework that integrates state-of-the-art attribution-based explainability techniques, including EigenCAM, EigenGrad-CAM, LayerCAM, and HiResCAM, into the YOLOv8 architecture. The proposed framework systematically analyses activation patterns within the model, generating fine-grained saliency maps that highlight the spatial regions most influential in brake light detection. Using a real-world vehicle dataset comprising diverse lighting and environmental conditions, the model is fine-tuned to predict binary brake light states (car_BrakeOn and car_BrakeOff). Comparative experiments demonstrate the ability of these CAM-based methods to provide interpretable visual explanations while maintaining detection accuracy. This study is the first to explore the integration of explainability techniques within YOLO-based brake light detection systems for semi-autonomous vehicles (SAE Levels 2 and 3), addressing a critical gap in the literature. By bridging the divide between black-box AI models and human-understandable reasoning, this research promotes transparency, accountability, and user trust in AI-driven perception systems for intelligent transportation. The findings contribute a foundational step toward the broader adoption of explainable and reliable AI in safety-critical SAV applications.

The manual and automated braking system is intended to prevent rear-end collisions. However, unforeseen malfunctions within the vehicle's system during braking could potentially cause accidents. Collision Warning (CW) systems are safety technologies in vehicles that aid drivers in preventing collisions. This paper introduces a fuzzy-assisted pre-warning system for brake testing that considers multiple factors contributing to brake failure, along with a post-failure CW algorithm tailored for heavy commercial vehicles. Simulations conducted using NetLogo demonstrate that accidents can be prevented during brake failure through appropriate adjustments in vehicle speed. The proposed collision avoidance system has been validated for its effectiveness, and the results are encouraging and enhance road user safety.

The manual and automated braking system is intended to prevent rear-end collisions. However, unforeseen malfunctions within the vehicle's system during braking could potentially cause accidents. Collision Warning (CW) systems are safety technologies in vehicles that aid drivers in preventing collisions. This paper introduces a fuzzy-assisted pre-warning system for brake testing that considers multiple factors contributing to brake failure, along with a post-failure CW algorithm tailored for heavy commercial vehicles. Simulations conducted using NetLogo demonstrate that accidents can be prevented during brake failure through appropriate adjustments in vehicle speed. The proposed collision avoidance system has been validated for its effectiveness, and the results are encouraging and enhance road user safety.

Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user’s data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions.

The primary objective of an autonomous vehicle (AV) is to provide a safe and driverless vehicle experience on the road. To accomplish collision free journey, an AV collects data from external entities such as camera, Lidar, radar, etc. and passes this data to a safety algorithm. This algorithm detects and monitors the steering, acceleration, and braking behavior of the vehicle. This can be a very dangerous situation if these internal components which control the behavior of AV fail to accept the instructions. Moreover, if there is a collision on the road, the AV must communicate and transfer data (position, direction, speed, alerts, etc.) with other AVs. There is a need to detect any type of failure in the AV in advance to ensure safety of all the users of the road. This chapter proposes a collision avoidance system for AVs. The proposed system introduces the concept of Meta Sensor in the AVs with fast communication technology Li-Fi to forward the message to leading vehicle (LV) to avoid collision. The aim is to detect the internal sensors behavior in advance with the use of Meta Sensor to avoid collisions. As soon, the failure of any internal sensor is detected, other AVs are immediately informed through exchange of messages and Li-Fi Technology. Emulation using NetLogo shows that in case of a sensor failure, an accident situation is avoided in minimum amount of time.

According to World Health Organization (WHO), the leading cause of fatalities and injuries is rear-ending collision in vehicles. The critical challenge of the technologically rich transportation system is to reduce the chances of accidents between vehicles. For this purpose, it is especially important to analyze the factors that are the cause of accidents. Based on these factors’ results, this paper presents a driver assistance system for collision avoidance. There are many factors involved in collisions in the existing literature from which we identified some factors which can affect the accident occurrence probability. However, with advancements in the technologies of autonomous vehicles, these factors can be controlled using an onboard driver assistance system. We used MATLAB’s Fuzzy Inference System Tool to analyze the categories of accident contributing factors. Fuzzy results are validated using the VOMAS agent in the NetLogo simulation model. The proposed system can inform the vehicle’s automated system when chances of an accident are higher so that the vehicle may take control from the driver. The proposed research is extremely helpful in handling various kinds of factors involved in accidents. The results of the experiments demonstrated that multi-factor-enabled vehicles could better avoid collision as compared to other vehicles.