Zeina Ali

In this paper, we propose a systematized analytical and AI-assisted framework to compute the end-to-end latency of an unmanned aerial vehicle (UAV)-attached wireless sensor network (WSN) node communicating over a 5G linked communication system and to identify and minimize the latency within...

Conventional Intelligent Speed Assistance (ISA) systems primarily rely on static map data or camerabased sign recognition, which limits their adaptability to dynamic driving conditions and real-time speed regulation. To address this gap, this paper proposes an AIbased Intelligent Speed Limiting System (ISLS) that integrates GPS, digital map APIs, and real-time vehicular data to predict and adjust vehicle speed proactively. The system employs a Long Short-Term Memory (LSTM) model for predictive speed adaptation based on upcoming road geometry, traffic context, and environmental inputs. A reinforcement learning-based control module ensures smooth and safe throttle or braking actions according to predicted limits. The design further incorporates hardware-level safety through isolation circuits and protective elements verified by simulation. A Python-based experimental testbed validates the proposed method in terms of response time and speed deviation; the results show advantages of the proposed method over the classical ISA systems. Hence, the proposed ISLS advanced a step closer to an adaptive, context-aware, and safety-sensitive speed control of the vehicle.

The evolution of autonomous vehicles (AVs) has captured widespread interest lately, offering prospects for enhanced road safety, alleviation of traffic congestion, and heightened fuel efficiency. The essential role of Controller Area Network (CAN) bus protocols in the operational integrity of AVs is undeniable. This research outlines the architecture of an elementary autonomous vehicle predicated on diverse CAN bus frameworks. We introduce a design that integrates a multiplexed CAN bus arrangement, fostering streamlined interactions among the AV's various subsystems. This design is inherently scalable, promoting ease of modification in step with evolving autonomous vehicular technologies. The paper delves into the intricacies encountered throughout the design phase and articulates the methodologies adopted to surmount these obstacles. Empirical simulations substantiate the efficacy of our design, underscoring its dependability and strength across a multitude of vehicular contexts. Fundamentally, this design lays the groundwork for the advancement of sophisticated AVs, thereby contributing to the realization of optimized vehicular ecosystems.

Background: Exteroceptive sensors on an autonomous vehicle require a high-performance communication bus. The number of exteroceptive sensors keeps rising, and the CAN bus, the most common intra-network bus in vehicles, cannot keep up. Objective This paper investigates the effect of Exteroceptive Sensors of Autonomous Vehicles on the CAN and CAN FD buses. Four types of sensors (4 cameras, 6 radars, 1 LiDAR, and 1 INS) have been introduced to create five different scenarios in two different test environments. Methods The simulation used a highly effective environment to obtain accurate measurements and results. Results and Conclusion The results showed that the LiDAR sensor has huge data and requires a high-efficiency bus; the CAN bus could not handle it, and the rest of the sensors can transfer their data through the traditional CAN bus