This paper introduces a novel reference model for intelligent longitudinal vehicle control, designed to enhance both safety and passenger comfort. The proposed model dynamically adjusts the follower vehicle’s acceleration based on its penetration distance relative to the lead vehicle, ensuring smooth speed transitions and adaptive deceleration. By preventing abrupt braking, the model maintains a safe inter-vehicle distance while reducing passenger discomfort. Key contributions include an analytical derivation of the follower vehicle’s dynamics and a novel formulation of the safety distance using the Lambert W function, enabling precise parameter optimization. A dedicated optimization framework ensures compliance with safety constraints while minimizing excessive acceleration and jerk. The model’s performance is validated through numerical simulations in various driving scenarios, including emergency braking, steady-speed following, variable-speed adaptation, and stop-and-go traffic. Results demonstrate its effectiveness in maintaining safety while enhancing ride comfort through gradual and controlled deceleration. The proposed approach is computationally efficient and well-suited for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. Future research will explore its integration with lateral control strategies, real-time adaptability, and machine learning techniques for further performance optimization in dynamic driving environments.

Malaria remains a significant global health challenge that affects more than 200 million people each year and disproportionately burdens regions with limited resources. Precise and timely diagnosis is critical for effective treatment and control. Traditional diagnostic approaches, including microscopy and rapid diagnostic tests (RDTs), encounter significant limitations such as reliance on skilled personnel, high costs and slow processing times. Advances in deep learning (DL) have demonstrated remarkable potential. They achieve diagnostic accuracies of up to 97% in automated malaria detection by employing convolutional neural networks (CNNs) and similar architectures to analyze blood smear images. This survey comprehensively reviews deep learning approaches for malaria detection and focuses on datasets, architectures and performance metrics. Publicly available datasets, such as the NIH and Delgado Dataset B are evaluated for size, diversity and limitations. Deep learning models which include ResNet, VGG, YOLO and lightweight architectures like MobileNet are analyzed for their strengths, scalability and suitability across various diagnostic scenarios. Key performance metrics such as sensitivity and computational efficiency are compared with models achieving sensitivity rates as high as 96%. Emerging smartphone-based diagnostic systems and multimodal data integration trends demonstrate significant potential to enhance accessibility in resource-limited settings. This survey examines key challenges and includes bias in the data set, generalization of the model and interpretability to identify research gaps and propose future directions to develop robust, scalable and clinically applicable deep learning solutions for malaria detection.