Vehicle Detection Accuracy Research Articles

PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.

Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving

This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle’s DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.

An Intelligent Traffic Analysis and Prediction System Using Deep Learning Technique

Accurate identification of vehicles and estimating density in traffic surveillance systems is a challenging task, particularly in scenarios with closely spaced lanes. Single Shot MultiBox Detector (SSD) is introduced in vehicle detection and classification due to its speed and accuracy. It utilizes a transfer learning technique that enables them to utilize features from pretrained Convolutional Neural Networks (CNNs). Although the utilization of multi-scale feature maps in SSD has achieved efficient results in vehicle detection, it struggles to identify key features of vehicles due to its one-stage detection approach. Furthermore, the use of VGG16 as the backbone network in these approaches leads to the loss of fine-grained details, posing challenges in accurately localizing and classifying small vehicles. Also, there is no interaction between high- and low-level features, which restricts the networks ability to effectively integrate and utilize both features for accurate vehicle detection. To overcome these limitations, an improved SSD approach is introduced in this paper, leveraging interactive multi-scale attention characteristics to accurately detect and classify vehicles. This approach utilizes ResNet50 as the network backbone to overcome the limitations of traditional SSDs in detecting small vehicles. Also, an attention block is incorporated into it to focus on key details and assign higher attention to relevant pixels within the feature map. Also, the network employs a parallel detection framework and shares multi-scale layers (both high and low), enabling efficient detection of vehicles with various sizes. Then, traffic density is estimated based on the weights assigned to the categorized vehicles obtained from the improved SSD and the recorded area. Finally, traffic is classified as high, low, or moderate by comparing the estimated density to a threshold value. By adding attention characteristics of different scales to the original detection branch and replacing the VGG16 with ResNet50 of the SSD technique using our method, the feature representation capability and detection accuracy are both significantly improved.

Accuracy Improvement of Automatic Smoky Diesel Vehicle Detection Using YOLO Model, Matching, and Refinement

The detection of smoky diesel vehicles is a key step in reducing air pollution from transportation. We propose a new method for identifying smoky vehicles that proceeds in three stages: (1) the detection of vehicle shapes, license plates, and smoke regions; (2) the implementation of the two matching techniques based on the smoke region–vehicle shape and smoke region–license plate relationships; and (3) the refinement of the smoke region detected. The first stage involves the evaluation of various You Only Look Once (YOLO) models to identify the best-fit model for object detection. YOLOv5s was the most effective, particularly for the smoke region prediction, achieving a precision of 91.4% and a mean average precision at 0.5 (mAP@0.5) of 91%. It also had the highest mean mAP@0.5 of 93.9% across all three classes. The application of the two matching techniques significantly reduced the rate of false negatives and enhanced the rate of true positives for the smoky diesel vehicles through the detection of their license plates. Moreover, a refinement process based on image processing theory was implemented, effectively eliminating incorrect smoke region predictions caused by vehicle shadows. As a result, our method achieved a detection rate of 97.45% and a precision of 93.50%, which are higher than that of the two existing popular methods, and produced an acceptable false alarm rate of 5.44%. Particularly, the proposed method substantially reduced the processing time to as low as 85 ms per image, compared to 140.3 and 182.6 ms per image in the two reference studies. In conclusion, the proposed method showed remarkable improvements in the accuracy, robustness, and feasibility of smoky diesel vehicle detection. Therefore, it offers potential to be applied in real-world situations.

Inter-Domain Invariant Cross-Domain Object Detection Using Style and Content Disentanglement for In-Vehicle Images

The accurate detection of relevant vehicles, pedestrians, and other targets on the road plays a crucial role in ensuring the safety of autonomous driving. In recent years, object detectors based on Transformers or CNNs have achieved excellent performance in the fully supervised paradigm. However, when the trained model is directly applied to unfamiliar scenes where the training data and testing data have different distributions statistically, the model’s performance may decrease dramatically. To address this issue, unsupervised domain adaptive object detection methods have been proposed. However, these methods often exhibit decreasing performance when the gap between the source and target domains increases. Previous works mainly focused on utilizing the style gap to reduce the domain gap while ignoring the content gap. To tackle this challenge, we introduce a novel method called IDI-SCD that effectively addresses both the style and content gaps simultaneously. Firstly, the domain gap is reduced by disentangling it into the style gap and content gap, generating corresponding intermediate domains in the meanwhile. Secondly, during training, we focus on one single domain gap at a time to achieve inter-domain invariance. That is, the content gap is tackled while maintaining the style gap, and vice versa. In addition, the style-invariant loss is used to narrow down the style gap, and the mean teacher self-training framework is used to narrow down the content gap. Finally, we introduce a multiscale fusion strategy to enhance the quality of pseudo-labels, which mainly focus on enhancing the detection performance for extreme-scale objects (very large or very small objects). We conduct extensive experiments on four mainstream datasets of in-vehicle images. The experimental results demonstrate the effectiveness of our method and its superiority over most of the existing methods.

VSL-Net: Voxel structure learning for 3D object detection

Current detection methods with single stage generally lack contextual structure information, the classification and location confidence are inconsistent, which are not able to achieve accurate dynamic multi-object detection. Therefore, a VSL-Net method is proposed based on single-stage detection framework, which maps voxels to the point cloud space through voxel structure learning for foreground point segmentation and center estimation. In the voxel feature extraction network, the submanifold sparse convolution is proposed to improve convolution efficiency by reducing operation of null region. In the detection network, a position alignment module is proposed to sample and interpolate in Bird’s Eye View. Characteristics of surrounding points are integrated to further improve the accuracy of detection. The proposed VSL-Net method was compared with other excellent method on the Kitti, Waymo and Nuscene datasets. The vehicle detection accuracy of VSL-Net reached 92.12%. Ablation and accuracy detection experiments have completed on a real physical platform. Experiment results showed that the proposed method had portability, strong generalization ability and high detection accuracy.

Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery

Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160 × 160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.

Implementation of Random Forest for Vehicle Type Classification using Gamma Correction Algorithm

Traffic control systems can be a valuable tool for monitoring road traffic by counting and tracking vehicles in real time. This research is the initial research into the development of methods to improve the accuracy of vehicle detection and recognition. The purpose of the study was to analyze the performance of gamma correction and random forest performance to improve the accuracy of vehicle detection and recognition. Performance measures used in the study were confusion matrix, accuracy, precision, recall, F1-score. Based on experimental results, random forest with gamma=1.5 got the best accuracy of 85.00%, while random forest with gamma=0.5 got accuracy of 81.30%, random forest with gamma=1.0 got accuracy of 84.00%, random forest with gamma=2.0 got accuracy of 84.00%.

ElegantYOLO: An Agile and Real-time Vehicle Detection System for Live Environments

Vehicle detection plays a crucial role in traffic monitoring systems, but it often faces challenges like occlusion, vehicle size, and shape variations. Existing systems struggle with misinterpreting parts of other vehicles as actual vehicles, leading to losses and distorted shapes. To address these issues, a new approach called ElegantYOLO is introduced in this study. ElegantYOLO combines elements from LittleYOLO-SPP and ShortYOLO-CSP while modifying baseline network layer depths using the improved Ghost Module Extended connection method to reduce computational costs. The model's learning capabilities are improved by incorporating spatial attributes through the concatenation of Spatial Pooling blocks. The study employs the Alpha-IoU as the bounding box loss function, minimizing the disparity between predicted and ground truth boxes. This enhances vehicle detection accuracy and robustness. Additionally, the study uses the slicing-aided hyper inference (SAHI) technique, which allows the lightweight backbone network to capture more detailed vehicle information by processing higher-resolution images. Through extensive testing on various datasets such as PASCAL VOC 2007, 2012, and MS COCO 2014, the proposed model not only excels in detecting small vehicles but also demonstrates improved detection accuracy across different environmental conditions. The performance of ElegantYOLO surpasses both LittleYOLO and ShortYOLO by achieving an almost 10% higher mean average precision (mAP). Specifically, the model achieves outstanding results on PASCAL VOC and COCO datasets, with mAPs of 96.45% and 79.28%, respectively. Moreover, the proposed technique significantly enhances accuracy while reducing detection time.

DSP-Based Traffic Target Detection for Intelligent Transportation

Internet of Things (IoT)-based intelligent transportation is attracting more and more attention. As a key component of intelligent transportation, traffic video monitoring is very important, in which vehicle and pedestrian detection on the road is a crucial task. Although vehicle and pedestrian detection through deep learning (DL) may achieve high accuracy, it tends to require high computing resources, which hinders its use on IoT devices. As an important class of IoT devices, digital signal processor (DSP) has the characteristics of low energy consumption, small size, and strong performance, which has been widely used in intelligent transportation. In order to use DL on DSP for accurate vehicle and pedestrian detection, we first propose a series of general tactics to optimize the object detection convolutional neural network (CNN) model, including convolution layer optimization, cache optimization, compiler optimization, intrinsics optimization and direct memory access (DMA) acceleration, and then a parallel scheme to extend the model to run on multicore, and further quantize the implementation of the model. We evaluate it on UA-DETRAC and KITTI datasets. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with only 0.06% accuracy loss.

Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification

The fusion of Internet of Things (IoT) and deep learning (DL) methods has proven valuable in automating vehicle detection and classification tasks on remote sensing images (RSI). This technology has broad applications, including traffic monitoring, urban planning, and transportation management. Recent advancements have demonstrated the efficacy of DL models like convolutional neural networks (CNN) in RSI classification tasks. In this aspect, this study proposes a novel honey badger optimization algorithm with an ensemble learning-based vehicle detection and classification (HBOAEL-VDC) technique. The purpose of the study is to design ensemble DL models for accurate vehicle identification and classification processes. To accomplish this, the HBOAEL-VDC technique makes use of an improved RetinaNet model for the detection of objects, i.e., vehicles on the RSI. Moreover, the classification of detected vehicles takes place using the ensemble learning process, comprising three DL models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). Furthermore, the HBOA-based parameter tuning process gets carried out to adjust the hyperparameter values of the DL models and thereby improve the classification results. The simulation outcome of the HBOAEL-VDC approach is tested on benchmark RSI databases. The experimentation outcomes reported the enhanced vehicle classification performance of the HBOAEL-VDC approach over other recent DL models.

Edge-Computing-Facilitated Nighttime Vehicle Detection Investigations With CLAHE-Enhanced Images

In this study, we propose a novel CLAHE-based nighttime image contrast enhancement approach for vehicle detection under nighttime conditions, which improves the contrast of low-quality nighttime images while preventing over-enhancement by employing the image dehazing technique. To implement and evaluate our proposed contrast enhancement method on nighttime images, we consider a scenario of using a camera-based Internet of Things (IoT)-edge computing device for traffic and road surveillance. Edge-computing and IoT technology enable significant amounts of novel studies to advance traffic system monitoring, sensing, control, and management. Considering multiple metrics of image enhancement quality, the proposed nighttime image contrast enhancement method outperforms some existing well-performing CLAHE-based methods. To provide accurate vehicle detection under nighttime conditions and different challenges, including vehicle overlapping, low-light conditions, camera vibrations, and image distortion, must be addressed. For this purpose, a deep neural network based on YOLOv5 architecture has been designed and trained using our custom-labeled dataset. The developed neural network is proven to be effective in the detection of different vehicles under low-light ambient conditions using video captured from a stationary camera. Experiments on our dataset show that the proposed contrast enhancement method greatly improves the detection performance of the trained YOLOv5 model under low-environment-light conditions compared with the model trained using unenhanced images. The model trained with enhanced images can provide an improvement of 5.7% on F1 score, 6.3% on mAP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{0.5,}$</tex-math> </inline-formula> and 3.4% on mAP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{0.5:0.95} $</tex-math> </inline-formula> under specific conditions.

Edge Intelligence Empowered Vehicle Detection and Image Segmentation for Autonomous Vehicles

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network, enhancing the high-bandwidth and low-latency of wireless data transmission with the multiplier effect of 5G and AI, greatly improving the edges’ processing speed. Through integrating EI and computer vision technology, video surveillance systems in ITS can improve the processing capability of traffic information, which improves traffic efficiency and ensures traffic safety. Accordingly, first, we propose an edge intelligence-based improved-YOLOv4 vehicle detection algorithm, introducing an efficient channel attention (ECA) mechanism and a high-resolution network (HRNet) to enhance vehicle detection ability. Second, an edge intelligence-based improved DeepLabv3+ image segmentation algorithm is proposed, replacing the original backbone network with MobileNetv2 and using the softpool method, thus reducing the network size while improving the segmentation accuracy. Experimental results show that our proposed model has a higher average precision (AP) and can improve vehicle detection accuracy from 82.03% to 86.22%. The mean intersection over union (mIOU) of the image segmentation model improves from 73.32% to 75.63%.

Smart Turns: Revolutionizing Road Safety

Abstract: A technological replacement for traditional convex mirrors at blind curves and T-junctions is presented in the abstract of this research article. The suggested system makes use of ultrasonic proximity sensors to see oncoming cars and sends timely notifications to drivers on both sides of the turn. The technology also computes and shows the oncoming vehicle's speed. A typical signal system or direct roadside projection of the alerts are both options provided by the system. This approach facilitates safer navigation at blind bends and T-junctions by using proximity sensors to measure vehicle speed and improve driver awareness. Our method improves accuracy, range, and reliability in vehicle detection and alerting by overcoming the limitations of convex mirrors. This technology improves safer driving by enhancing driver awareness and reducing accidents.

DESPP-DETR: A Dense Connection Efficient Spatial Pooling DEtection TRansformer for Vehicle Detection

Real-time vehicle detection is a challenging and vital task in intelligent transportation systems. The key requirements for a vehicle detection model are speed and accuracy. However, existing real-time vehicle detection models often sacrifice one of these qualities in favor of the other. This trade-off makes them unfit for real-time deployment, where both speed and accuracy are equally important. Additionally, occlusion, which refers to the obstruction or partial covering of vehicles, further complicates detection and affects the system’s accuracy. In this study, we propose DESPP-DETR, a one-stage detection network for real-time vehicle detection. It is based on bipartite matching and a transformer encoder-decoder architecture, with the addition of a dense connection block and enhanced spatial pyramid pooling. The presence of dense connection block strengthens feature extraction. The enhanced spatial pyramid pooling eliminates the fixed-size constraint and increases the network’s learning capacity. When compared to existing models, DESPP-DETR achieves greater accuracy in real-time vehicle detection. On the MS COCO 2017 dataset, the proposed model achieves an improved mean average precision (mAP) of 75.53%, making it a promising solution for intelligent transportation systems.

YOLO-FA: Type-1 fuzzy attention based YOLO detector for vehicle detection

Vehicle detection is an important component of intelligent transportation systems and autonomous driving. However, in real-world vehicle detection scenarios, the presence of many complex and high uncertainty factors, such as illumination differences, motion blur, occlusion, weather, etc., makes accurate and real-time vehicle detection still challenging. In order to reduce the influence of these uncertainties in real scenarios and improve the accuracy and real-time performance of vehicle detection, this paper proposes a type-1 fuzzy attention (T1FA), in which fuzzy entropy is introduced to re-weight the feature map in order to reduce the uncertainty of the feature map and facilitates the detector’s focus on the target center as a way to effectively improve the accuracy of vehicle detection. Furthermore, to detect vehicles with different sizes more effectively, mixed depth convolution in MetaFormer (MDFormer) is employed as a token mixer to capture multi-scale perceptual fields. And a novel YOLO detector based on fuzzy attention (YOLO-FA) is proposed. Experimental results show that T1FA can boost 3.2% AP50 on challenging vehicle detection dataset UA-DETRAC, which is better than other commonly used attention mechanisms, especially in scenarios of rain and nighttime with higher uncertainty by 4.2% and 8.1% AP50, respectively. Finally, without pretraining on extra data, YOLO-FA achieves 70.0% AP50 and 50.3% AP on UA-DETRAC, which achieves better balance between accuracy and speed compared with state-of-the-art detectors. The remarkable improvement of T1FA in different detectors and datasets also shows the considerable generalization of T1FA.

Improvement of Vehicle detection and classification performance with Region of Interest

This Abstract-Paper presents an innovative approach to improve the performance of vehicle detection and classification in the context of computer vision. This study proposes the use of Region of Interest (ROI) to identify and process vehicle information in traffic images. The proposed method is based on a combination of deep learning-based object detection algorithms with a more focused ROI-based approach. First, we use state-of-the-art convolution neural networks to perform object detection in traffic images, which involves a visual recognition process to identify vehicle locations. Then, using ROI, we reorder and reduce the dimensions of irrelevant data to remove background areas and increase attention to vehicle objects. This aims to reduce the computational load and speed up the detection and classification processes. We evaluate the proposed approach on a wide traffic dataset and compare the results with conventional object detection approaches without using ROI. The experimental results show that the proposed method outperforms the conventional approach in terms of vehicle detection speed and accuracy. In conclusion, this study succeeded in improving the performance of vehicle detection and classification by leveraging Region of Interest, which allows better focus on relevant regions and reduces computational complexity. These results can make an important contribution to the development of a more efficient and reliable traffic control and transportation system.

Blockchain-Enabled Trust Management Model for the Internet of Vehicles

The high-speed movement of nodes and the burstiness of interactions in the Internet of Vehicles pose huge challenges to the trusted vehicle collaboration and data sharing. Aiming at the disadvantages of existing authentication mechanisms and trust management models for connected vehicles, this paper proposes a trust management model enabled by blockchain to ensure the traceability, non-tampering, unforgeability, and transparency of vehicular interactions. The proposed trust management model leverages Dirichlet distribution, reputation regression, and revocation punishment to objectively and accurately reflect the trust status of vehicles. Simulation results on real-world datasets show that the proposed trust management model advantageously improves the accuracy of malicious vehicle detection and the attack resistance of connected vehicles.