Cellular Vehicle-to-Everything (C-V2X) Mode 4, developed by 3GPP, supports vehicle-to-vehicle communication without a cellular base station via LTE sidelink. The messages transmitted in the Internet of Vehicles (IoV) include periodic single-hop broadcast Beacon messages and multi-hop broadcast Emergency messages triggered by events. In this paper, we analyze the impact of multi-hop broadcast of Emergency messages in C-V2X Mode 4 on the performance of Beacon messages and propose collision probability models for Beacon messages and Emergency messages. The non-periodicity nature and lower broadcast latency requirement of the multi-hop Emergency messages may cause resource allocation conflicts in the resource selection process of C-V2X Mode 4, thus result in forwarding collision of Emergency messages and collision of subsequent Beacon messages. Therefore, we propose several multi-hop broadcast schemes for Emergency messages to reduce the collision probability by assigning independent resource grants for Emergency messages and adjusting the number of forwarding nodes. The performance of Beacon messages and Emergency messages of several different forwarding schemes proposed are simulated. The simulation results show that the proposed broadcasting schemes significantly improve the performance of Beacon messages and Emergency messages.

With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model’s focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model’s practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection.

To undercover the distinct impact of heterogeneous strategies on information diffusion, this paper develops a double-layer network to stimulate the information propagation process of individual with the enthusiasm psychology and the assimilation avoidance effect. The enthusiasm psychology is captured by an increasing adoption function and the assimilation avoidance effect is modeled by an oscillating adoption function. In the proposed double-layer network, an susceptible state(S-state) node accumulates information from its adopted state(A-state) neighbors and may evolve into the A-state according to the adoption probabilities in each layer. Then, the nodes keep the same state in the two layers anytime. Finally, the A-state may transition to the recovery state(R-state) if it receives the information and adopt it. The R-state node quits the information diffusion and does not transmit nor receive any information. This study analyzes the proportion of nodes in the R-state and their relative variance to characterize the information diffusion mechanism applying both theoretical and numerical methods. Results show that enhancing the individual’s enthusiasm psychology and the assimilation avoidance effect can accelerate the information propagation. When the two layers experience outbreaks that do not occur simultaneously, the propagation range exhibits a first-order discontinuous increase. When there are a dominate layer in the process of information spreading, the propagation range shows a second continuous boundary.

Traffic object detection based on camera sensors is a critical task for autonomous vehicles. However, in nighttime conditions with adverse lighting, several challenges arise: blurred object edges, large-scale variations, and complex lighting conditions involving both overexposure and underexposure. As a result, it remains difficult for vision-based perception tasks to ensure reliable precision and rapid inference simultaneously. This paper proposes a novel, efficient, and lightweight vision module for detecting traffic objects in challenging nighttime environments, developed by enhancing the YOLOv8n architecture. Firstly, a bidirectional weighted feature fusion method (BiFPN) is incorporated in the path aggregation network, and an additional shallow P2 feature map is introduced to fully utilize key information from features at different scales. Then, the coordinate attention (CA) module is inserted between the end of the feature pyramid and the detection head to capture both semantic and spatial information of the object. Finally, the dynamic upsampler (DySample) is employed to guide the model in focusing on the detailed features of challenging samples, thereby balancing accuracy across different object categories. A subset of nighttime traffic scenes is curated from the BDD100K dataset for the evaluation of the proposed approach. The experiments demonstrate that, relative to the baseline, our method raises the mean average precision (${\mathrm{mAP}}_{50}$) from 51.5% to 56.6%, achieves a 7.3% decrease in parameter quantity, and maintains a fast inference speed of 208 FPS. For the challenging bike and motorbike categories, notable improvements in detection accuracy are achieved. Compared with other advanced YOLO-series models such as YOLOv11, the proposed model also exhibits significant performance advantages with a 3.7% higher ${\mathrm{mAP}}_{50}$. Furthermore, our model demonstrates good generalization performance on the larger BDD100K nighttime partition. The findings confirm that our approach significantly improves detection accuracy without compromising real-time processing, highlighting its potential as a lightweight vision module providing reliable perceptual inputs for autonomous vehicle control and safety actuators in challenging nighttime scenarios.

We propose and analyze a statistical beamforming design for the downlink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system when the receivers have perfect knowledge of channel state information of their links, whereas the transmitter has only the statistical information about its user's link (i.e., covariance matrix of the vector channel). We analytically derive the ergodic sum rate in terms of the covariance matrices of the channel links and subsequently provide the optimal beamforming designs by maximizing the ergodic sum rate for the extreme cases of low SINR and high SINR regimes. In addition, the impact of imperfect successive interference cancellation (SIC) is analyzed, and the corresponding performance bounds are characterized through low and high SINR analysis. The accuracy of our analytical ergodic sum rate derivation is confirmed by comparing the analytical results with Monte Carlo simulation results. The results demonstrate that the ergodic sum rate increases with the total transmit power and decreases with the power coefficient of near users. It is also demonstrated that MISO-NOMA can significantly enhance the ergodic sum rate as compared to the conventional MISO-OMA.

The Internet of Vehicles (IoV) can transmit the status information of vehicles and roads through single-hop or multi-hop broadcast communication, which is a key technology for building intelligent transportation systems and enhancing road safety. However, in dense traffic environments, broadcasting Emergency messages via vehicles can easily trigger massive forwarding redundancy, leading to channel resource selection conflicts between vehicles and affecting the reliability of inter-vehicle communication. This paper analyzes the forwarding near the single-hop transmission radius boundary of the sending node in a probability-based inter-vehicle multi-hop forwarding scheme, pointing out the existence of the boundary forwarding redundancy problem. To address this problem, this paper proposes two probability-based schemes with boundary optimization: (1) By optimizing the forwarding probability distribution outside the transmission radius boundary of the sending node, the forwarding nodes outside the boundary can be effectively utilized while effectively reducing the forwarding redundancy they bring. (2) Additional forwarding backoff timers are allocated to nodes outside the transmission radius boundary of the sending node based on the distance to further reduce the forwarding redundancy outside the boundary. Experimental results show that, compared with the reference schemes without boundary forwarding probability optimization, the proposed schemes significantly reduce forwarding redundancy of Emergency messages while maintaining good single-hop and multi-hop transmission performance. When the reference transmission radius is 300 m and the vehicle density is 0.18 veh/m, compared with the probability-based forwarding scheme without boundary optimization, the proposed schemes (1) and (2) improve the single-hop packet delivery ratio by an average of about 5.41% and 11.83% and reduce the multi-hop forwarding ratio by about 18.07% and 36.07%, respectively.