In this paper, we propose a Wi-Fi positioning method based on Deep Learning (DL). To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural Network (DNN) structure that is capable of learning reliable features from a large set of noisy samples and avoids the need for hand-engineering. Also, to maintain the temporal coherence, a Hidden Markov Model (HMM) based fine localizer is introduced to smooth the positioning result obtained from the immediate estimation of DNN-based coarse localizer. The data required for the experiments is collected from the real world in different periods to meet the actual environment. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy in case of the turbulent wireless signals.

This study presents a TD3-driven Bilateral Control Model (TD3-BCM) aimed at improving the stability of mixed traffic flows in autonomous vehicle (AV) chains. By integrating deep reinforcement learning, TD3-BCM optimizes control strategies to reduce traffic oscillations, smooth speed and acceleration fluctuations, and enhance overall system performance. Stability analysis shows that TD3-BCM effectively suppresses traffic fluctuations, with system stability improving from 1.132 to 1.182 as AV penetration increases. At an AV penetration rate of 40%, TD3-BCM surpasses both Cooperative Adaptive Cruise Control (CACC) and traditional Bilateral Control Model (BCM) approaches in terms of traffic efficiency, safety, and energy use—raising trailing vehicle speed by 12.6%, shortening average headway by 19.0%, increasing Time-to-Collision (TTC) by 87.3%, and lowering fuel consumption by 14.8%. When AV penetration reaches 70%, fuel savings rise to 19.7%, accompanied by further improvements in both traffic stability and safety. TD3-BCM provides a scalable and sustainable solution for intelligent transportation systems, particularly in high-penetration AV environments, by significantly enhancing stability, operational efficiency, and road safety.

Traffic oscillations degrade efficiency, increase safety risks, and lead to excessive energy consumption. To address this, we propose the Bilateral Control Model with Deep Reinforcement Learning (BCM-DRL), integrating Deep Reinforcement Learning (DRL), specifically the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to suppress oscillations and enhance stability. Using the I-80 Next Generation Simulation (NGSIM) dataset, BCM-DRL is trained and evaluated against Bilateral Control Model (BCM) and Car-Following Model with Deep Reinforcement Learning (CFM-DRL). Simulation results show that BCM-DRL reduces the cumulative damping ratio by 75%, decreases fuel consumption by 21.6%, and achieves near-zero Time-to-Instability (TIT) values, significantly improving stability and efficiency. These findings validate BCM-DRL as an effective approach to mitigating traffic oscillations and optimizing vehicle control.