Autonomous connected vehicles (ACV) involve advanced control strategies to effectively balance safety, efficiency, energy consumption, and passenger comfort. This research introduces a deep reinforcement learning (DRL)-based car-following (CF) framework employing the Deep Deterministic Policy Gradient (DDPG) algorithm, which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning. Utilizing real-world driving data from the highD dataset, the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios. The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control (MPC-ACC) controller. Results show that the DRL model significantly enhances safety, achieving zero collisions and a higher average time-to-collision (TTC) of 8.45 s, compared to 5.67 s for MPC and 6.12 s for human drivers. For efficiency, the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases. In terms of energy optimization, the proposed approach reduces fuel consumption by 5.4% relative to MPC. Additionally, it enhances passenger comfort by lowering jerk values by 65%, achieving 0.12 m/s³ vs. 0.34 m/s³ for human drivers. A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics. Moreover, the findings underscore the potential of DRL in advancing autonomous vehicle control, offering a robust and sustainable solution for safer, more efficient, and more comfortable transportation systems.

Object detection, a major challenge in computer vision and pattern recognition, plays a significant part in many applications, crossing artificial intelligence, face recognition, and autonomous driving. It involves focusing on identifying the detection, localization, and categorization of targets in images. A particularly important emerging task is distinguishing real animals from toy replicas in real-time, mostly for smart camera systems in both urban and natural environments. However, that difficult task is affected by factors such as showing angle, occlusion, light intensity, variations, and texture differences. To tackle these challenges, this paper recommends Group Sparse YOLOv8 (You Only Look Once version 8), an improved real-time object detection algorithm that improves YOLOv8 by integrating group sparsity regularization. This adjustment improves efficiency and accuracy while utilizing the computational costs and power consumption, including a frame selection approach. And a hybrid parallel processing method that merges pipelining with dataflow strategies to improve the performance. Established using a custom dataset of toy and real animal images along with well-known datasets, namely ImageNet, MSCOCO, and CIFAR-10/100. The combination of Group Sparsity with YOLOv8 shows high detection accuracy with lower latency. Here provides a real and resource-efficient solution for intelligent camera systems and improves real-time object detection and classification in environments, differentiating between real and toy animals.