Vehicle Information Research Articles

Can Historical Accident Data Improve Sustainable Urban Traffic Safety? A Predictive Modeling Study

Traffic safety is a critical factor for the sustainable development of urban transportation systems. This study investigates the impact of historical accident information on the prediction of future traffic accident risks, as well as the interaction between this information and other features, such as driver violations and vehicle attributes. Using a comprehensive dataset of traffic accidents involving passenger vehicles in a western Chinese city, we developed two predictive models: Model 1, which is based on vehicle information and driver violations, and Model 2, which integrates historical accident data. The results indicate that the inclusion of historical accident information significantly enhances the predictive performance of the model, particularly in terms of AUC (Area Under the Curve) and AP (Average Precision) values. Furthermore, through feature importance analysis and SHAP (SHapley Additive exPlanations) value evaluation, this study reveals the interaction effects between historical accident data and other features, and how these interactions influence model decisions. The findings suggest that historical accident data play a positive role in predicting future accident risk, with varying effects on risk mitigation. These insights provide a scientific basis for developing strategies to ensure the sustainable development of urban transportation systems.

Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.

Pattern detection in the vehicular activity of bus rapid transit systems

In this paper, we explore different methods to detect patterns in the activity of bus rapid transit (BRT) systems focusing on two aspects of transit: infrastructure and the movement of vehicles. To this end, we analyze records of velocity and position of each active vehicle in nine BRT systems located in the Americas. We detect collective patterns that characterize each BRT system obtained from the statistical analysis of velocities in the entire system (global scale) and at specific zones (local scale). We analyze the velocity records at the local scale applying the Kullback-Leibler divergence to compare the vehicular activity between zones. This information is organized in a similarity matrix that can be represented as a network of zones. The resulting structure for each system is analyzed using network science methods. In particular, by implementing community detection algorithms on networks, we obtain different groups of zones characterized by similarities in the movement of vehicles. Our findings show that the representation of the dataset with information of vehicles as a network is a useful tool to characterize at different scales the activity of BRT systems when geolocalized records of vehicular movement are available. This general approach can be implemented in the analysis of other public transportation systems.

Design of a dual-focal-plane head-up display system using double freeform mirrors

This paper presents the design of a dual-focal-plane head-up display (HUD) system utilizing double freeform mirrors. The system is specifically developed for automotive applications, aiming to enhance driving safety and experience by projecting vehicle information within the driver’s line of sight. Compared to the traditional WHUD, the ARHUD offers a broader field of view and a greater virtual image distance. If the ARHUD system has double focal planes, it can separately display basic and interactive driving information to drivers, which is currently a significant research direction in the ARHUD system design. The proposed design only uses double freeform mirrors and uses a single display source for both focal planes, thus reducing the overall system complexity, assembly difficulty, and cost. Detailed design methods and results are discussed, showcasing the system’s good imaging performance and compact structure, which provide valuable insights for the development of next-generation dual-focal-plane HUDs for vehicles. The proposed system demonstrates significant advancements in reducing fabrication and testing difficulties compared to traditional dual-focal-plane HUD designs. The study’s findings contribute to the future development of dual-focal-plane head-up display systems.

Combining geometrical and intensity information to recognize vehicles from super-high density UAV-LiDAR point clouds

The traditional airborne LiDAR point clouds vehicle recognition algorithms only utilize the contained geometric information and are not sufficient to recognize vehicles accurately, especially in complex urban scenes. And their applicability in super-high density UAV-LiDAR point clouds is unknown. Therefore, a 3-D algorithm combining geometric and intensity information from super-high density UAV-LiDAR point clouds is presented. The proposed algorithm firstly converts the original point clouds into a 3D multivalued image which fuses intensity, elevation and density information simultaneously. Thereafter, the potential vehicle voxels are extracted according to the intensity, elevation and density consistency of vehicles. Subsequently, individual vehicles are recognized using the potential vehicle voxel’s spatially connected set with vehicle size constraint. Finally, the quantified attribute information of each individual vehicle, containing the spatial location, type, and size, is determined. The results for recognized vehicles are evaluated using UAV-LiDAR data with different densities and demonstrate an average quality (Kappa coefficient) of 96.58% (96.04%) without being significantly affected by occlusion and the very close vehicle arrangement.

Intelligent Connected Vehicle Information Security Risks and Countermeasures

The development of intelligent connected vehicles has promoted the upgrading of the transportation system, but it also brings challenges in information security. This paper analyzes the current information security vulnerabilities of intelligent connected vehicles, including attack methods, security vulnerabilities, and protective measures. Firstly, through the analysis of the existing attack methods and their principles, the potential security vulnerabilities and threats are summarized. Then, based on security and cryptography technology, relevant measures for information security protection are proposed to achieve all-round protection of intelligent connected vehicle information. Finally, The paper puts forward the measures to solve the information security problem of intelligent connected vehicle from three aspects: Encryption and Authentication, Firewall and Intrusion Detection System, and Over-the-air (OTA).

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

A Blockchain‐Based Proxy Re‐Encryption Scheme With Cryptographic Reverse Firewall for IoV

ABSTRACTAs the internet of vehicles (IoV) technology develops, it promotes the intelligent interaction among vehicles, roadside units, and the environment. Nevertheless, it also brings vehicle information security challenges. In recent years, vehicle data sharing is suffering to algorithm substitution attacks (ASA), which means backdoor adversaries can carry out filtering attacks through data sharing. Therefore, this paper designs a blockchain‐based proxy re‐encryption (PRE) scheme with cryptographic reverse firewall (BIBPR‐CRF) for IoV. In our proposal, CRF can promise the internal safety of vehicle units. More specifically, it can prevent ASA attacks while ensuring chosen plaintext attack (CPA)‐security. Meanwhile, the PRE algorithm can provide the confidential sharing and secure operation of data. Moreover, we use a consortium blockchain service center (CBSC) to store the first ciphertext and re‐encrypt it with smart contracts on the blockchain, which can avoid single point of failure and achieve higher efficiency compared to proxy servers. Finally, we evaluate the performance of BIBPR‐CRF with regard to communication cost, computational cost, and energy consumption. Our proposal is the most fitting for IoV application, in contrast with the other three schemes.

Congested traffic patterns of mixed lattice hydrodynamic model combining the perceptual range differences with passing effect

With the aid of Vehicle-to-everything (V2X) technologies for network connectivity, connected autonomous vehicles (CAVs) can broaden the drivers' perceptual boundaries and receive a greater quantity of exogenous vehicle information, thereby governing the vehicle's acceleration information of the next moment. Nonetheless, constrained by contemporary communication networks and sophisticated vehicle control technology, the process of promoting CAVs is long-lasting, and throughout this stage of transition, both CAVs and human-driven vehicles (HDVs) will happen on the road. Moreover, passing behaviour is uncommon in the traffic flow model despite being a fundamental microscopic driving behaviour, particularly within mixed-traffic situations. To bridge that hole, we introduce the percentage ratios of CAVs into the lattice hydrodynamic model by integrating the perceptual range differences between two different types of vehicles with passing effects. Subsequently, the stability norm associated with the new model is ascertained by performing the linear stability analysis. When the above-mentioned stability condition is not achieved, we investigate the complex behaviour of the new model, and the associated existing conditions, as well as the modified Korteweg-de Vries (mKdV) equation, are determined simultaneously. When the passing ratio is inadequate, no jam and kink jam make up the whole phase region; while when the passing ratio surpasses the minimum, the initial unstable region can be segregated into two extra segments: the chaotic sub-region and the kink jam sub-region, the density wave progressively transitioned from being a kink-Bando traffic wave to a chaotic phase. Lastly, the findings of the numerical experiment identify with the theoretical derivation made above.

A heterogeneous continuum traffic flow model considering mixed connected and autonomous vehicles

Under the background of mixed scenarios of Connected and Autonomous Vehicles and Connected and Autonomous Electric Vehicles, a novel mixed car-following model considering the information of multiple vehicles and the difference of safety spacing in different driving scenarios is proposed. Then, the corresponding heterogeneous continuum traffic flow model is derived. Secondly, through the linear and nonlinear analysis approaches, the linear stability condition and the KdV-Burgers equation are yielded to describe the characteristics of traffic flow evolution. Finally, numerical simulations are performed to explore the density evolution of traffic flow under different initial density and permeability conditions. The conclusions can be summarized as follows: (1) The derived model can well simulate spatiotemporal phase changes and traffic flow phenomena such as stop-and-go waves and local cluster effects. (2) With the increase of the permeability of electric vehicles and speed difference sensitivity coefficient, the road density decreases from 0.15veh/m to 0.06veh/m, the traffic congestion area decreases, and the traffic congestion is effectively suppressed, which also verifies the rationality of the above theory.

Wireless sensor fusion localization method for target vehicles in a collaborative environment of vehicle road trajectory information

Abstract Vehicle positioning technology is the foundation for achieving traffic management. Vehicle positioning errors are significant in complex environments, making it challenging to ensure positioning accuracy. To reduce positioning errors, this paper explores a wireless sensor fusion localization method for target vehicles in a collaborative environment of vehicle road trajectory information. Based on wireless sensor data, we achieve collaborative environmental perception of vehicle and road trajectory information and construct a target vehicle trajectory optimization model. Through a fuzzy neural network, data from different sensors are integrated to overcome the problem of limited positioning accuracy of a single sensor in a complex environment. The experimental results show that both the heading angle and rolling angle errors float between −0.1° and 0.1°, and the positioning error is less than 0.1 m, which proves that the method has high positioning accuracy and good application value.

Cooperative control of multi-vehicle formation based on cooperative game

In this paper, an optimization decision-making method of vehicle fleet queue based on game theory is proposed. By adopting the upper and lower controller structure, the longitudinal motion stability problem of the vehicle fleet is solved. The upper controller utilizes a game control algorithm specially developed for this study. The game payoff function is constructed by considering real-time vehicle information and comprehensively evaluating factors such as expected distance, vehicle safety, and stability. The optimal expected acceleration of the vehicle is determined. Subsequently, the lower controller adjusts the engine throttle opening or brake pressure to regulate the vehicle operation according to the desired acceleration. Simulation results show that the designed game strategy can effectively reduce vehicle spacing and speed errors, thus significantly improving the stability as well as safety of fleet operation.

RANCANG BANGUN PURWARUPA SISTEM REGISTRASI MASUK PINTU TOLL UNTUK KENDARAAN MENGGUNAKAN LONG RANGE RADIO (LORA)

The inefficiency of toll gate systems is one of the main causes of traffic congestion on toll roads. This condition requires an innovative solution to detect and process vehicle information before the vehicle reaches the toll gate. This study aims to develop a prototype that uses ESP32, LoRa, GPS, OLED, and Internet of Things (IoT) technology to provide a solution to this problem. The developed prototype uses ESP32 as the main microcontroller, LoRa for long-distance communication, GPS to track the vehicle's location, and OLED as the information display. This system is designed for vehicles to perform real-time registration. The test results show that this prototype can function well in registering vehicle data that will enter the toll gate remotely. With the implementation of this technology, it is expected to reduce the waiting time for toll road users caused by the long queue times of vehicles at conventional toll gate registration systems.

Estimation and modeling of pedestrian dilemma zone at uncontrolled intersections under mixed traffic conditions

Objectives Pedestrian gap acceptance (PGA) theory is the basic concept for pedestrian dilemma zone (PDZ) analysis and modeling and their gap acceptance behavior depends on dilemma behavior also. Uncontrolled intersections are one of the major locations where pedestrians have more dilemma and there is a possibility of an interaction between the pedestrian and vehicle due to the incorrect decision taken by the pedestrian when the vehicle lies within the limits of PDZ. Elimination and modeling of spatial boundaries of pedestrian dilemma stage improve the PGA. Methods The present study is intended to quantify and model the pedestrian dilemma zone (PDZ) boundaries at uncontrolled X-intersections under mixed traffic conditions. Video data were collected from four four-legged uncontrolled intersections in India. Pedestrian and vehicle information was extracted using DataFromSky software and manually from video. The Cumulative Gap Distribution (GCD) and Support Vector Machine (SVM) methods were used to estimate the boundaries of PDZ and developed a binary logistic regression model to estimate the PDZ boundary limits. Results The lower boundary limits of PDZ using GCD and SVM methods are 9.0 m and 6.0, respectively, and the upper boundary limits of PDZ using GCD and SVM methods are 16.5 and 18.5 m, respectively. The GCD method overestimated the lower limit and underestimated the upper limit compared with the SVM method. The binary logistic regression model results confirmed that pedestrian age, gender and crossing speed have a negative correlation and location of pedestrian crossing, vehicle type, and approaching speed have a positive correlation with the boundary limits of PDZ. Conclusions From the present study, it is concluded that SVM better estimated the PDZ boundary limits with the largest margin compared to the GCD method. It is concluded that the boundary limits shift away from the intersection in the case of female and old-age pedestrians compared to male and young-age pedestrians, respectively. The size benefit in the case of 2Ws is the reason for shifting the PDZ boundary limits close to the crosswalk. The lower approaching speeds of the vehicles at uncontrolled intersections are the reason for pedestrians accept the gap at shorter distances.

LB-CLAS: Lattice-based conditional privacy-preserving certificateless aggregate signature scheme for VANET

The rapid development of vehicular ad-hoc networks (VANETs) has brought great convenience to intelligent transportation, and the secure transmission of information in VANETs has become a serious problem. In addition, the protection of private information of vehicles is also a key issue. Aiming at the problem of how to guarantee the secure transmission of information in VANETs under the condition of satisfying security and privacy, we propose a lattice-based conditional privacy-preserving certificateless aggregate signature scheme (LB-CLAS) for VANETs. Instead of using Number Theory Research Unit (NTRU) lattices and discrete Gaussian sampling, the proposed LB-CLAS scheme is based on algebraic lattice. In addition, based on the module version of Small Integer Solution (MSIS) and module version of Learning With Error (MLWE) hard problems, we prove that the LB-CLAS scheme is existential unforgeability under adaptively chosen message attacks (EUF-CMA). Our LB-CLAS scheme employs individual signature verification in vehicle-to-vehicle (V2V) mode, while utilizing aggregate signatures and batch verification in vehicle-to-infrastructure (V2I) mode, with slightly differing transmission parameters between the two modes. Based on Dilithium, our LB-CLAS scheme solves the problem of high storage overhead and computational cost of existing schemes. The performance analysis shows that our LB-CLAS scheme is more efficient in terms of computation cost, storage overhead, and power consumption compared to existing schemes. Compared with existing schemes, our LB-CLAS scheme reduces the signature and verification overheads by more than 17.6% and 43.4%, respectively. Our LB-CLAS program also has significant advantages in batch verification. As the number of vehicles increases, our batch certification time cost is reduced by more than 90%. In addition, our LB-CLAS scheme has the smallest signature length, with a signature size that is 1X smaller than the most efficient existing scheme for the same level of security.

Adaptive Frame Structure Design for Sensing-Assisted Downlink Communication in the Vehicle-to-Infrastructure Scenario.

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance.

Versatile Lamellar Wrap-Structured PVDF/PZT/CNTs Piezoelectric Sensor for Road Traffic Information Sensing, Monitoring, and Energy Harvesting

Active monitoring and accurate sensing of road traffic information provide crucial data for road managers and traffic law enforcement. However, current sensors and monitoring systems struggle to capture comprehensive vehicle information simultaneously. In the study, a novel piezoelectric sensor using PVDF/PZT/CNTs composites was developed for actively monitoring road traffic information and explored for energy harvesting suitability. Piezoelectric composites with a lamellar wrapped structure and excellent localized conductive networks were prepared through solution blending, non-solvent-induced phase separation, and layered hot pressing. The results revealed that this structural design improves the proportions, crystallinity, and content of the β-phase crystals in the composites, while significantly enhancing their piezoelectric properties, dielectric properties, and electronegativity. When the content of CNTs was 3.0 wt%, the proportion and content of β-phase crystals in the composites reached the highest values (91.36 % and 62.34 %), respectively. Additionally, the open-circuit voltage (Voc) and the short-circuit current (Isc) were 3.36 and 6.16 times higher, respectively, than those of the undoped CNTs composites. The developed piezoelectric sensor shows a wide pressure monitoring range (0.1 MPa–2.1 MPa), excellent sensitivity (6.94 MPa−1 at (<0.6 MPa) and 12.35 MPa−1 at (0.6–2.1 MPa)). As a vehicle non-stop weighing sensor, it presents extremely fast response (15 ms) and recovery (16 ms) speeds with long-term stability of 500,000 cycles. The most important feature of the developed piezoelectric sensor is its dual function as both a sensor and an energy harvester. Furthermore, the developed piezoelectric sensor can actively monitor vehicle axle weight, speed, and wheelbase while capturing energy from vehicle movement. This work opens up innovative solutions for road traffic monitoring and vehicle movement energy harvesting, with promising applications in intelligent transportation.

Fault diagnosis and analysis of automobile capacitive touch display false triggering

Abstract Nowadays, automobile handling is mainly concentrated in the central control display. The use of high-definition and large-size capacitive touch displays has become the mainstream trend. Driving information and entertainment information are displayed on it, and the operation of the electronic control function of the car is generally realized by touching the touch panel of the central control display. As the integration of the central control display becomes higher, the fault of the display needs to be paid more attention to. The common faults include display faults and touch faults. The display fault will affect the display of vehicle information, and the driver cannot obtain vehicle status information; the touch fault will affect the car operation and can not carry out human-machine interaction. Therefore, once the central control display fails, it will affect the safety driving of the car. This paper studies the false triggering failure which is a kind of touch fault of the vehicle’s 9-inch capacitive touch display. Firstly, we test the parasitic capacitance of faulty parts and repeat the touch failure. After testing and analyzing, it has been found that the mechanical structure design of the display and the air gap bonding process can change the value of parasitic capacitance which leads to false triggering. This paper proposes solutions to solve this failure which are changing the wideness of glue to avoid glue overflow and strengthening size management to improve the spacing between the touch panel and the TFT display screen. After the implementation of the solutions, the false triggering failure no longer occurs, which proves that the solutions are effective and feasible.

Analyzing the Suitability of Vehicle Telematics Data as a Surrogate Safety Measure for Short-Term Crashes

Traffic safety analysis relies heavily on the comprehensiveness of traffic crash data. However, crash occurrences are rare events. Researchers have sought alternative measures closely linked to crash occurrences, known as surrogate safety measures, to address this challenge. The advent of vehicle telematics has introduced a valuable source of data, consisting of time-stamped positional information of vehicles, facilitated through telecommunication networks. Wejo Data Services Inc. collects vehicle telematics data through collaboration with various automotive original equipment manufacturers (OEMs) to furnish connected vehicle data along with driver event variables, such as braking and acceleration. This study seeks to investigate the relationship between hard-braking events and the incidence of total crashes and peak period crashes across different facility types using analysis of variance and negative binomial models. The results show a strong correlation between hard braking and crash frequency. The ratio of hard-braking events to crashes exhibits notable variations across peak hours, off-peak periods, and nighttime. Further, the coefficient of hard braking is significantly higher on freeways compared with non-freeways. The model performance of different short period models improved better than the extent of improvement in total crash model after hard braking was introduced as an explanatory variable. The marginal effects of hard braking on crash frequency on freeways are consistently higher than those in non-freeway segments.

Research on Vehicle Pose Detection Method Based on a Roadside Unit.

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles.