Intelligent Transportation Systems Technologies Research Articles

Towards 6G vehicular networks: Vision, technologies, and open challenges

As an essential method to meet the communication requirements of intelligent transportation and automated driving, vehicular networks facilitate intelligent interaction and cooperation among vehicles and pedestrians, vehicles, infrastructures, and cloud platforms. Enabled by 4G and 5G communications, cellular vehicle to everything (C-V2X)-based vehicular networks have been widely applied in safety and efficiency improvement scenarios of intelligent transportation systems (ITS). However, the development of autonomous driving and ITS technologies requires the support of lower latency and more reliable communications to realize advanced driving applications such as platooning and sensor-sharing, which raises higher requirements for the performance of vehicular networks. Towards 2030 and beyond, 6G communications will construct intelligent connections for everything and provide the ultimate performance, thus 6G-enabled vehicular networks have been widely researched. This paper surveys and categorizes a comprehensive array of existing studies on the intersection of 6G and vehicular networks. Drawing insights from these studies, we delineate the evolutionary trajectory of 6G and vehicular networks, outlining a visionary perspective on their forthcoming development. Furthermore, we summarize and analyze the existing studies from the perspective of enabling technologies such as communication, intelligence, and security. Finally, by analyzing the characteristics of the existing works, we provide the future challenges and potential research directions for 6G vehicular networks.

Research on autonomous driving technology in intelligent transportation systems

Abstract. The convergence of scientific and technological advancement with the acceleration of urbanization has resulted in a notable intensification of the issue of traffic congestion. In this context, the development of autonomous driving technology has emerged as a pivotal area of focus for future transportation systems. Therefore, this paper presents a review of the current status of automatic driving technology in intelligent transportation systems, based on methods such as literature, data analysis and case studies. Furthermore, it delves into the evolution, applications, challenges and solutions of automated driving technologies in intelligent transport systems, and presents forward-thinking insights into the future development of intelligent transportation. As autonomous driving technology continues to evolve, the integration of deep reinforcement learning and other advanced technologies will facilitate the advancement of intelligent vehicle human-machine collaborative decision-making technology. It is imperative that the construction technology of safety-critical scenarios be reinforced in order to enhance the interpretability of algorithmic models and the real-time nature of scenario generation. All parties need to collaborate to promote the innovation and improvement of autonomous driving technology, achieve widespread application and sustainable development, and create a better travelling environment.

Exploration and Application Analysis of Autonomous Driving Technology

Abstract. In recent years, with the acceleration of urbanization and the increase in vehicle ownership, traffic congestion has become an increasingly prominent issue. As vehicle technology advances, autonomous driving vehicles have gradually entered the public eye. Autonomous driving technology, seen as an important solution to the problem of traffic congestion, has garnered widespread attention and application. This paper employs a literature review and case analysis method to comprehensively explore the development history, core components, and challenges faced in the practical application of autonomous driving technology. It details the practical examples of companies such as Tesla, Google Waymo, and Baidu Apollo, fully showcasing the key role and vast potential of autonomous driving technology in intelligent transportation systems. Through a comprehensive analysis of the application of autonomous driving technology, this paper provides an in-depth introduction to its current applications, aiming to offer a more systematic knowledge framework and address the limitations of a single-perspective approach.

Smart traffic monitoring with YOLOv9 object detection algorithm

Rapid advancements in artificial intelligence technology have enabled computer vision to be utilized across a wide range of engineering disciplines. This study examines the practical solutions offered by image processing technology in manual counting applications and the accuracy of advanced algorithms. The applicability and performance of the YOLOv9 algorithm in traffic counts have been evaluated. The research shows that the algorithm operates with high accuracy and minimizes human error. The study involves classification and counting operations for three different types of vehicles. According to the results, cars and trucks are detected with over 95% accuracy, while smaller objects like motorcycles have slightly lower accuracy. The effective use of YOLOv9 in vehicle counting and traffic management applications highlights the importance of object detection technology in intelligent transportation systems. This study demonstrates the potential of this technology to improve efficiency in traffic management, providing guidance for future applications. The key role that advanced algorithms like YOLOv9 can play in the development of intelligent transportation systems is an important topic for future researchers and industry professionals.

Efficient license plate recognition in unconstrained scenarios

Automatic license plate recognition (ALPR) is a critical technology for intelligent transportation systems. Most existing ALPR methods are focused on specific application scenarios. Although there are a few methods that focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient ALPR (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems with high efficiency. We design a light LPD structure based on efficient object detection methods and use anchor-free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations and a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method on five datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, our method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficient-alpr-unconstrained.

DDGformer: Direction- and distance-aware graph transformer for traffic flow prediction

Being an indispensable core technology of intelligent transportation systems, accurate traffic flow prediction contributes to improving people’s travel efficiency and safety. How to accurately model the dynamic spatio-temporal correlation in traffic data is a key challenge in traffic flow prediction. The models based on self-attention and Graph Neural Networks (GNN) have shown great potential in addressing this challenge. However, existing methods ignore the relative distance and direction of traffic flow series, and cannot accurately capture the real dynamic spatio-temporal correlations in traffic systems. To overcome this limitation, we propose a novel framework called Direction- and Distance-aware Graph Transformer (DDGformer). Specifically, it utilizes a self-attention module that simultaneously perceives direction and distance, enabling it to identify the relative position and direction of the original traffic flow series, and models long-term temporal correlations. Additionally, a dynamically enhanced adaptive graph convolution network is designed to capture dynamic traffic patterns in traffic systems. Extensive experimental results on four real-world traffic datasets show that our approach is overall more competitive and exhibits outstanding computational efficiency.

Evaluating current and future pedestrian mid-block crossing safety treatments using virtual reality simulation

Virtual reality (VR) simulation offers a proactive, cost effective, immersive, and low risk platform for studying pedestrian safety. Within immersive virtual environments (IVEs), existing and alternative design conditions and intelligent transportation systems (ITS) technologies can be directly compared, prior to real-world implementation, to assess the impacts alternatives may have on pedestrian safety, perception, and behavior. Environmental factors can be controlled within IVEs so that test trials are replicable and directly comparable. Coupled with stated preference feedback, participants’ observed preferences and behavior provide a comprehensive understanding of the impacts of proposed design alternatives. This research presents a case study of pedestrian behavior with three different mid-block crossing safety treatments modeled within a one-to-one scale IVE replication of a real-world location in Charlottesville, Virginia. The three safety treatments consider both passive and active collision avoidance designs and technologies, including (1) the existing painted crosswalk, (2) the addition of rectangular rapid flashing beacons (RRFBs), and (3) a pedestrian to everything (P2X) ITS phone application. Additionally, this paper demonstrates a VR simulation experimental design and framework for testing pedestrian safety treatments within naturalistic and replicable IVEs to assess both stated and observed preferences and behaviors of pedestrians. Repeated measures ANOVA indicated changes in both accepted gap size (p = 0.001) and crossing speed (p < 0.001) with alternative safety treatments. Generalized mixed models showed that pedestrians waited for statistically larger gap sizes (p = 0.02) without the assistance of alternative safety technologies (RRFBs and P2X application) and pedestrians crossed the street significantly faster (p = 0.001) without the alternative safety technologies, leading to unsafe dashing behavior. Through post-experiment surveys, it was found that participants perceived the As Built environment to be the least safe of the three treatments and that their sense of risk within the IVE was realistic. Considering both the observed crossing behavior and stated feedback, pedestrians exhibited intentionally unsafe darting behavior without assistive safety technology. This study demonstrates how VR simulation may be leveraged to study both stated preferences and observed behavior for understanding the safety implications of alternative roadway designs, providing a proactive approach for assessing and designing for pedestrian safety.

Smart adaptive technologies for intelligent transportation systems (ITS): efficient design through BIM integration

PurposeWhile efficient design in engineering projects is crucial, this paper aims to examine the integration of Building Information Modeling (BIM) into railway Intelligent Transportation Systems (ITS). The paper provides some key understanding of integrating BIM and ITS to improve the efficiency of railway infrastructure projects.Design/methodology/approachAn in-depth qualitative analysis of three ITS case studies was conducted to understand BIM’s global impact and benefits in railway infrastructure projects. While case study one investigated the Crossrail (UK), the other two case studies were TUC Rail (Belgium) and the Intercity railway network (Norway).FindingsThe findings include the specific benefits of BIM, regarding the railway infrastructure. The result indicated that BIM benefits were consistently the same across all case studies. Although Case study 1 was the only one that boasts a high reduction in waste and reworks, all of the case studies showed less rework and delays due to BIM. The results indicated that the advantages of BIM for such projects are cost optimization, reduction in waste, rework and lessening delays. Subsequently, this leads to the ease and efficiency with which structures and railways can be built. The outcomes can ultimately assist transportation planners in better planning and managing railway projects.Practical implicationsThis study proposes the integration of BIM into railway projects as a part of their ITS. The BIM integration into railway projects as a part of their ITS fits within the overall planning to handover phases. Specifically, the BIM integration improves the design process of typical railway projects. Thus, the most significant advantage of BIM for railway projects is to further improve their design process leading to a higher degree of constructability.Originality/valueRailway infrastructure performs a major role in economic and regional development. The complexity of railway projects continues to increase as the need for more railway infrastructure is on the rise globally. BIM is proving to be an effective tool for improving the efficiency of railway infrastructure projects. As the utilization of BIM is intensifying, the railway industry can further exploit BIM to improve project delivery adeptness by offering greater collaboration leading to efficient design processes. As a result, the understanding of BIM for horizontal projects such as railway infrastructure on a global scale is a substantial exercise that this research aims to respond to.

Self-powered triboelectric sensor based on a carbon fiber/glass fiber/epoxy structural composite for efficient traffic monitoring

Intelligent transportation systems (ITSs) are being investigated as potential solutions for traffic congestion. Triboelectric sensors (TESs) have gained prominence among ITS technologies owing to their efficiency and self-powered capabilities in detecting external changes through friction-generated electrical signals. Herein, a traffic monitoring system using a fiber-reinforced plastic-based TES (FRP-TES) is proposed, designed with high-strength and high-stiffness fiber-reinforced plastic (FRP) as the core material. FRP is a structural composite comprising high-strength fibers impregnated with engineering resin. When the surface of the epoxy, which has a charged layer, reacts with the charged tire, electrostatic induction occurs in the carbon fibers. By leveraging the principles of carbon and glass fiber/epoxy-reinforced infrastructure, along with corresponding electrical properties, we analyze electrical signals produced when a tire traverses the FRP-TES. We subject the FRP-TES to electrical durability and tensile tests to verify signal stability and mechanical properties (strength ≤ 1770.87 MPa, modulus ≤ 41.38 GPa). The analysis proposes an effective method for obtaining the tire position, movement direction, speed, and acceleration using paired FRP-TES units. The proposed approach achieves significant reduction in maximum errors (0.52 % in speed, 3.24 % in acceleration) with eight evenly spaced FRP-TES units in a 240 mm section. A larger FRP-TES is fabricated to demonstrate practicality for personal mobility. Incorporating FRP-TES units into road infrastructure can enhance structural stability while providing reliable real-time data for traffic monitoring, accident response, and prediction.

Intelligent Transportation System Technologies, Challenges and Security

Intelligent Transportation Systems (ITS) first appeared in 1868 with traffic lights. With developing technology, the need to bring a smart approach to transportation applications within the scope of speed and environmental protection has emerged. Protecting ITS infrastructure against cyber attacks has become a matter of reputation for states. It is essential to provide the necessary technological infrastructure for the integrated operation of the systems used in ITS, especially geographical location, communication, and mapping. These technological developments bring cyber attacks, risks, and many dangers that should be avoided, especially on the systems used. This study examines ITS architecture, applications, communication technologies, and new trend technologies in detail. This study includes contributing to studies in the field of ITS and preventing attacks and incidents that may occur in terms of cyber security. The most important cyber attacks that may occur in ITS applications are included. In addition, the minimum security requirements that can be taken in ITS applications and infrastructures against these attacks are included.

Stripe-Assisted Global Transformer and Spatial–Temporal Enhancement for Vehicle Re-Identification

As a core technology in intelligent transportation systems, vehicle re-identification has attracted growing attention. Most existing methods use CNNs to extract global and local features from vehicle images and roughly integrate them for identifying vehicles, addressing intra-class similarity and inter-class difference. However, a significant challenge arises from redundant information between global and local features and possible misalignment among local features, resulting in suboptimal efficiency when combined. To further improve vehicle re-identification, we propose a stripe-assisted global transformer (SaGT) method, which leverages a dual-branch network based on transformers to learn a discriminative whole representation for each vehicle image. Specifically, one branch exploits a standard transformer layer to extract a global feature, while the other branch employs a stripe feature module (SFM) to construct stripe-based features. To further facilitate the effective incorporation of local information into the learning process of the global feature, we introduce a novel stripe-assisted global loss (SaGL), which combines ID losses to optimize the model. Considering redundancy, we only use the global feature for inference, as we enhance the whole representation with stripe-specific details. Finally, we introduce a spatial-temporal probability (STPro) to provide a complementary metric for robust vehicle re-identification. Extensive and comprehensive evaluations on two public datasets validate the effectiveness and superiority of our proposed method.

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

Analisis Jaringan 4G untuk Aplikasi Smart Ambulance

Intelligent Transport System (ITS) is an intelligent traffic system used for traffic management and engineering. The ITS network uses inter-vehicle telecommunication technology. The city of Semarang is one of the cities that will establish ITS technology. One area that applies technology is health. The telecommunications system applied in the health sector today allows medical staff to treat patients in ambulances before arriving at the hospital. This communication system needs to be supported by a good cellular network so that data can be sent to the server. This study aims to measure QOS (Quality of Service) including the characteristics of delay, packet loss, jitter, and throughput from various cellular providers. The results of data measurements using wireshark software with TIPHON standard guidelines obtained an average value for delay obtained by Provider A with the best result of 86.22 ms. The average value for packet loss obtained from the measurement results from the Penggaron Terminal to the City Hall Bus Stop was obtained by Provider A with the best result of 0.474% d for the City Hall Bus Stop to the Penggaron Terminal obtained by Provider B with the best result of 0.574%. The average value of jitter obtained from the measurement results from the Penggaron Terminal to the City Hall Bus Stop was obtained by Provider B with the best result of 0.42 ms, for the City Hall Bus Stop to the Penggaron Terminal, Provider A was obtained with the best result of 0.57 ms. The average throughput value obtained from the measurement results for Penggaron Terminal to City Hall Bus Stop was obtained by Provider C with the best result of 6631 Kbps, for City Hall Bus Stop to Penggaron Terminal obtained by Provider B with the best result of 6339.5 Kbps. Based on this, knowing the value of delay, packet loss, jitter and throughput is expected to be a consideration for the development of a smart ambulance system in the city of Semarang. There are two measurement points, namely the Penggaron Terminal to the City Hall Bus Stop and the City Hall Bus Stop to the Penggaron Terminal using the Semarang BRT.

Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach

Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each technique has tradeoffs between precision and processing speed. Two-stage detectors like Faster R-CNN are highly accurate but slow at 7 FPS. Single-shot detectors like SSD are faster at 22 FPS but less precise. YOLO is extremely fast at 45 FPS but has lower accuracy. This paper reviews prominent deep learning vehicle detectors. It proposes a new integrated method combining YOLOv3 detection, optical flow tracking and trajectory analysis to enhance both accuracy and speed. Results on highway and urban datasets show improved precision, recall and F1 scores compared to YOLOv3 alone. Optical flow helps filter noise and recover missed detections. Trajectory analysis enables consistent object IDs across frames. Compared to other CNN models, the proposed technique achieves a better balance of real-time performance and accuracy. Occlusion handling and small object detection remain open challenges. In summary, deep learning has enabled major progress but enhancements in model architecture, training data and occlusion handling are needed to realize the full potential for traffic management applications. The integrated method proposed offers improved performance over baseline detectors. We have achieved 99 % accuracy in our project

A Lightweight, Efficient, and Physically Secure Key Agreement Authentication Protocol for Vehicular Networks

In the contemporary era, Vehicular Ad Hoc Networks (VANETs) have emerged as a vital technology in intelligent transportation systems, substantially enhancing the overall travel experience by providing advanced services to vehicles while ensuring driver safety. Despite the notable improvements, the inherent complexity of VANETs presents persistent security challenges, encompassing issues such as privacy preservation for vehicles, message authentication, and constraints in computational power and network bandwidth. Various authentication protocols have been designed for VANETs. However, many of these protocols exhibit significant vulnerabilities, rendering them insecure and unreliable in the face of diverse security threats, such as denial of service, replay, forgery, and impersonation attacks. Moreover, some existing schemes encounter limitations, including high computational complexity and the introduction of additional communication overhead and computational costs. To tackle these concerns, we designed a lightweight and secure identity authentication protocol based on elliptic curve cryptography with the objective of furnishing an effective and secure data transmission mechanism across a public communication channel for the Internet of Vehicles. In addition, we introduce Physically Unclonable Functions (PUFs) to ensure physical layer security during the communication process. A detailed security analysis demonstrates that the proposed protocol is resilient against various attacks. Through a comparative analysis with existing relevant protocols, in scenarios with a high density of vehicles, the algorithm demonstrates significantly lower computational costs and communication overhead than the related protocols, indicating that the proposed protocol is lightweight and efficient. Consequently, the empirical findings indicate that our protocol surpasses others in terms of reliability, user convenience, and practicality for ensuring secure data transmission within VANETs.

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions.

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing-a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems.

A novel deviation measurement for scheduled intelligent transportation system via comparative spatial-temporal path networks

Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status. However, the presentation of the data lacks structural information. Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously. Therefore, there is a need for complementary methods to address these deficiencies. To address these limitations, this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system. A dual information network is constructed to assess the degree of operational deviation considering the planning tasks. To validate the effectiveness, discussions are conducted through a modified cosine similarity calculation on theoretical analysis, delay level description, and the ability to identify abnormal dates. Compared to some state-of-the-art methods, the proposed method achieves an average Spearman delay correlation of 0.85 and a relative distance of 3.47. Furthermore, case analyses are invested in regions of China's Mainland, Europe, and the United States, investigating both the overall and sub-regional network fluctuations. To represent the impact of network fluctuations in sub-regions, a response loss value was developed. The times that are prone to fluctuations are also discussed through the classification of time series data. The research can offer a novel approach to system monitoring, providing a research direction that utilizes individual data combined to represent macroscopic states. Our code will be released at https://github.com/daozhong/STPN.git.

Secrecy Analysis of ABCom-Based Intelligent Transportation Systems With Jamming

Employing ambient backscatter communication (AmBC) technology in Intelligent Transportation Systems (ITS) has emerged as an appealing solution to boost the awareness of crosswalks. However, the AmBC-based ITS is expected to face serious security threats due to the presence of malicious eavesdroppers. In this paper, we investigate the secure multi-antenna transmission in an AmBC-based ITS coexisting with a passive eavesdropper with jamming. Specifically, a cooperative jammer is placed in the system to deliberately disrupt the eavesdropper without affecting the legitimate receiver. In order to characterize the performance of the proposed scheme, new approximate closed-form expressions of secrecy outage probability (SOP) are derived by adopting the Gauss-Chebyshev quadrature. Additionally, the asymptotic behavior of SOP at the high signal-to-noise ratio (SNR) regime is also studied to provide more insights into the system design. We also derive the asymptotic SOP, when the number of transmit antennas tends to infinity. Monte Carlo simulations are provided to demonstrate the validity of our analytical results and to show that 1) the secrecy performance can be significantly improved by allocating part of the transmit power to perform cooperative jamming and 2) the optimal power allocation factor is related to the total transmit power.

Relay-node Selection Method Based on Weighted Strategy for 3D Scenario in Internet of Vehicles

&lt;p&gt;In recent years, Internet of Vehicles (IoV), as a supporting technology for Intelligent Transportation System (ITS), is flourishing with the emergence and development of new technologies such as edge computing, 5G communication, and Artificial Intelligence (AI). However, the more complexity of wireless channels and vehicle distribution in 3D scenario brings a great challenge for relay-node selection in ITS. In this paper, we focus on how to alleviate the problem that the decline of two-hop distance and two-hop connection probability caused by the relative fading of inter-layer communication radius in 3D scenario with Vehicle-to-Vehicle (V2V) communication. To face this challenge, we develop a Relay-node Selection method based on Weighted Strategy for Overpass scenario (RSWSO). The simulation results show that RSWSO achieves improvement in two-hop distance, and an increase of up to 11.2% in terms of two-hop connection probability.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene

To address the comprehensive optimization problem of driving performance and fuel economy in the driving process of hybrid electric vehicles (HEV) in the car-following scene in the connected environment, an energy management strategy (EMS) based on front vehicle speed prediction and ego vehicle speed planning is designed by combining intelligent transportation system (ITS) technology. The front vehicle speed predictor is first established based on the long short-term memory neural network (LSTM). Then, based on the predicted speed of the front car, the predictive cruise control (PCC) strategy is designed for realizing the speed control in the car-following scene by combining it with the adaptive cruise control (ACC). Finally, based on the planned vehicle speed, deep reinforcement learning (DRL)-based EMS is used to optimize the power distribution among different power components of HEVs. The analysis of simulation results under the SUMO-Python joint simulation platform verifies the proposed strategy.