Vehicle Safety Applications Research Articles

Vehicle Safety Application through the Integration of Flood Detection and Safe Overtaking in Vehicular Communication

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience. Doi: 10.28991/CEJ-2024-010-09-015 Full Text: PDF

Context-aware resource allocation for vehicle-to-vehicle communications in cellular-V2X networks

Cellular Vehicle-to-Everything (C-V2X) networks provide critical support for intelligently connected vehicles (ICVs) and intelligent transport systems (ITS). C-V2X utilizes vehicle-to-vehicle (V2V) communication technology to exchange safety–critical information among neighbors. V2V communication has stringent high-reliability and low-latency requirements. The existing solutions on resource allocation for V2V communications mainly rely on channel states to optimize resource utilization but fail to consider vehicle safety requirements, which cannot satisfy safety application performance. In this paper, we focus on application-driven channel resource allocation strategy for V2V communications. First, we propose an inter-packet reception model to represent the delay between two consecutive and successful reception packets at a receiver. We then design an application-specific utility function where the utility depends on the packet reception performance and vehicle safety context. Finally, we formulate the channel resource allocation problem as a non-cooperative game model. The game model can guide each node to cooperate and achieve the trade-off between fairness and efficiency in channel resource allocation. The simulation results show that our work can significantly improve the reliability of V2V communications and guarantee the vehicle safety application performance.

Optimization techniques for IDS‐Generated traffic congestion control in VANET

AbstractVehicular Ad‐hoc Network (VANET) is an emerging field of wireless networks that enables a variety of vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable and secure communication among nodes. However, IDS requires a significant amount of data to process for monitoring intrusive activities in the network. As a result, the volume of traffic increases, resulting in the network congestion. Motivated by this fact, this study provides an overview of the optimization techniques for VANET traffic congestion control. It discusses a state‐of‐the‐art analysis along with the requirements for IDS‐generated traffic congestion control. It highlights the congestion control approaches for the traffic generated by an IDS and identifies the challenges in this domain. This study also proposes a novel IDS framework for reducing IDS‐generated network traffic by combining the Local Outlier Factor and Random Forest classifier. The proposed study achieved a high precision while yielding low false positive and false negative rates. The study outperformed the existing studies with an increase in accuracy of 1.16% and a reduction in attack detection time of 1.1869 seconds. Additionally, it discusses the possible future research directions that can be applied to address the issues of IDS‐generated traffic congestion. Overall, this study serves as a comprehensive guide to the current status of IDS‐generated traffic congestion control and diverse approaches to lessen it that can be employed by academicians and researchers.

Anomaly Detection Against GPS Spoofing Attacks on Connected and Autonomous Vehicles Using Learning From Demonstration

GPS spoofing attacks pose great challenges to connected vehicle (CVs) safety applications and localization of autonomous vehicles (AVs). In this paper, we propose to utilize transportation and vehicle engineering domain knowledge to detect GPS spoofing attacks towards CVs and AVs. A novel detection method using learning from demonstration is developed, which can be implemented in both vehicles and at the transportation infrastructure. A computational-efficient driving model, which can be learned from historical trajectories of the vehicles, is constructed to predict normal driving behaviors. Then a statistical method is developed to measure the dissimilarities between the observed trajectory and the predicted normal trajectory for anomaly detection. We validate the proposed method using two threat models (i.e., attacks targeting the multi-sensor fusion system of AVs and attacks targeting the intersection movement assist application of CVs) on two real-world datasets (i.e., KAIST and Michigan roundabout dataset). Results show that the proposed model is able to detect almost all of the attacks in time with low false positive and false negative rates.

On the performance analysis of NOMA-based vehicular visible light communication systems

Communication-based vehicle safety applications have emerged as one of the best solutions to enhance road safety. In this area, visible light communication (VLC) has great potential for such applications, named Vehicular VLC (V-VLC). V-VLC is preferred due to its highly secure, low complexity, and radio frequency (RF) interference-free characteristics, exploiting the line of sight (LoS) propagation of visible light and usage of already existing vehicle light-emitting diodes. This research addresses the application of the non-orthogonal multiple access (NOMA) technique in V-VLC based vehicle-to-vehicle communication. In this study, we proposed a system integrating NOMA in V-VLC networks and comprehensively provided an analysis of the system. Subsequently, we derived theoretical achievable capacity and an exact bit error rate expression for the proposed scenario. This paper also covers discussions about system performance under the non-ideal NOAM at the receiver, the effect of transmitter-receiver distance, and the impact of different weather conditions. The derived analytical BER and channel capacity expressions are verified with the simulation results.

Attitude in Motion: Constraints Aided Accurate Vehicle Orientation Tracking in Harsh Environment

Accurate vehicle orientation tracking in 3 dimensions or 3D attitude tracking, is an essential requirement for various vehicle stability and safety applications. In this paper, we consider the problem of vehicle attitude tracking along with its external acceleration estimation using low cost inertial measurement units. The current state of the art approaches are unable to estimate orientation when the body is in motion for a prolonged period, as the accelerometer measurements are corrupted by external accelerations that are produced due to body movements. In this paper, a novel filtering framework is proposed to eliminate the acceleration induced uncertainty for body observing severe and prolonged external acceleration, which is based on linear Kalman filter. We show that the sensor constraints allow the removal of external acceleration from all all of its axes. This approach is unique and superior from current state of the art as it estimates the acceleration without using any information from additional sensors such as wheel encoder, GPS, and/or camera. A unique state formulation is proposed which incorporate magnetometer to compliment accelerometer in rotation matrix frame work. Simulations and real world experimental tests are conducted to verify the performance of the proposed algorithm in various dynamic conditions.

Increasing Vehicular Visible Light Communications Range Based on LED Current Overdriving and Variable Pulse Position Modulation: Concept and Experimental Validation

Due to its unique advantages, the integration of Visible Light Communications (VLC) in vehicle safety applications has become a major research topic. Nevertheless, as this is an emergent technology, several challenges must be addressed. One of the most important of these challenges is oriented toward increasing vehicular VLC systems' communication range. In this context, this article proposes a novel approach that provides a significant communication distance enhancement. Different from most existing works on this topic, which are based on refining the VLC receiver, this new article is focused on improving the VLC system based on the benefits that can be achieved through the VLC transmitter. The concept is based on Light-Emitting Diode (LED) current overdriving and a modified Variable Pulse Position Modulation (VPPM). Therefore, LED current overdriving provides the VLC receiver higher instantaneous received optical power and improved Signal-to-Noise Ratio (SNR), whereas the use of the VPPM ensures that the VLC transmitter respects eye regulation norms and offers LED protection against overheating. The concept has been experimentally tested in laboratory conditions. The experimental results confirmed the viability of the concept, showing an increase of the communication range by up to 370%, while maintaining the same overall optical irradiance at the VLC transmitter level. Therefore, this new approach has the potential to enable vehicular VLC ranges that cover the requirements of communication-based vehicle safety applications. To the best of our knowledge, this concept has not been previously exploited in vehicular VLC applications.

Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

AbstractVehicle‐to‐Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non‐line‐of‐sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context‐Aware Target Classification (CA‐TC) module coupled with a hybrid learning‐based predictive modeling technique for CVS systems is proposed. The CA‐TC consists of two modules: a Context‐Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA‐TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real‐world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on‐the‐fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

Calibrating stochastic traffic simulation models for safety and operational measures based on vehicle conflict distributions obtained from aerial and traffic camera videos

Proper calibration process is of considerable importance for traffic safety evaluations using simulation models. Allowing for a pure with and without comparison under identical circumstances that is not directly testable in the field, microsimulation-based approach has drawn considerable attention for the performance evaluation of emerging technologies, such as connected vehicle (CV) safety applications. Different from the traditional approaches to evaluate mobility impacts, safety evaluations of such applications demand the simulation models to be well calibrated to match real-world safety conditions. This paper proposes a novel calibration framework which combines traffic conflict techniques and multi-objective stochastic optimization so that the operational and safety measures can be calibrated simultaneously. The conflict distribution of different severity levels categorized by time-to-collision (TTC) is applied as the safety performance measure. Simultaneous perturbation stochastic approximation (SPSA) algorithm, which can efficiently approximate the gradient of the multi-objective stochastic loss function, is used for model parameters optimization that minimizes the total simulation error of both operational and safety performance measures. The proposed calibration methodology is implemented using an open-source software SUMO on a simulation network of the Flatbush Avenue corridor in Brooklyn, NY. 17 key parameters are calibrated using the SPSA algorithm and are compared with the real-world traffic conflicts extracted using vehicle trajectories from 14 h’ high-resolution aerial and traffic surveillance videos. Representative days are identified to create variation envelopes for performance measures. Four acceptability criteria, including control for time-variant outliers and inliers, bounded dynamic absolute and system errors are adopted for results analysis. The results show that the calibrated parameters can significantly improve the performance of the simulation model to represent real-world safety conditions (i.e., traffic conflicts) as well as operational conditions. The case study also demonstrates the usefulness of aerial imagery and the applicability of the proposed model calibration framework, so the calibrated model can be used to evaluate the safety benefits of CV applications more accurately.

‘Grey-Box-Processing’: a novel validation method for use in vehicle safety applications

The ‘Grey-Box-Processing’ method, presented in this article, allows for the integration of simulated and experimental data sets with the overall objective of a comprehensive validation of simulation methods and models. This integration leads to so-called hybrid data sets. They allow for a spatially and temporally resolved identification and quantitative assessment of deviations between experimental observations and results of corresponding finite element simulations in the field of vehicle safety. This is achieved by the iterative generation of a synthetic, dynamic solution corridor in the finite element domain, which is deduced from experimental observations and restricts the freedom of movement of a virtually analyzed structure. The hybrid data sets thus contain physically based information about the interaction (e.g. acting forces) between the solution corridor and the virtually analyzed structure. An additional result of the ‘Grey-Box-Processing’ is the complemented three-dimensional reconstruction of the incomplete experimental observations (e.g. two-dimensional X-ray movies). The extensive data sets can be used not only for the assessment of the similarity between experiment and simulation, but also for the efficient derivation of improvement measures in order to increase the predictive power of the used model or method if necessary. In this study, the approach is presented in detail. Simulation-based investigations are conducted using generic test setups as well as realistic pedestrian safety test cases. These investigations show the general applicability of the method as well as the significant informative value and interpretability of generated hybrid data sets.

Space Division Multiple Access for Cellular V2X Communications

Vehicular communication is the backbone of future Intelligent Transportation Systems (ITS). It offers a network-based solution for vehicle safety, cooperative awareness, and traffic management applications. For safety applications, Basic Safety Messages (BSM) containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic map. BSMs are shared using mode 4 of Cellular V2X (C-V2X) communications in which resources are allocated in an ad hoc manner. However, the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network, thus reducing the reliability of safety applications. Moreover, as vehicles choose the transmission resources in a distributed manner in mode 4 of C-V2X, the packet collision problem is further aggravated. This paper presents a novel solution in the form of a Space Division Multiple Access (SDMA) protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node interferers. The proposed protocol works by dividing road segments into clusters and sub-clusters. Several sub-frames are allocated to a cluster and these sub-frames are reused after a certain distance. Within a cluster, sub-channels are allocated to sub-clusters. We implement the proposed SDMA protocol and evaluate its performance in a highway vehicular network. Simulation results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling (SB-SPS) in terms of safety range and packet delay.

TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4.

Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

A Statistical Approach Toward Channel Modeling With Application to Large-Scale Censored Data

Connected vehicle safety and efficiency applications rely on vehicular wireless networks for information sharing. To study the performance of these networks, and numerous protocols designed for them, it is essential to be able to model the channel behavior. However, due to the complexity of the vehicular environment, such modeling has to rely on data collected from large scale field tests. A majority of data collection campaigns with a large number of vehicles utilize vehicular on-board units (transceivers) to collect data in the form of received signal strength (RSS) values. Such datasets are limited in information since only the RSS values of correctly received packets are recorded, leading to datasets that can be considered as censored; especially where packet losses are high. Therefore, deriving channel models that accurately represent the channel behavior at distances with considerable loss of packets is a challenge. In this paper, we propose Bernoulli Mapping Estimation, a novel approach for the estimation of distributions from censored samples. Additionally, we provide a general method and formulation for modeling the communication channel in vehicular environments, considering receiver characteristics, imperfections, and data sparsity. The proposed methods were applied to datasets collected from highway and intersection environments; and the derived models are shown to be accurate representations of the datasets. It is also shown that even with only a small fraction of the RSS values, the framework is able to produce fading models that are similar in characteristics to field data.

A Front Water Recognition Method Based on Image Data for Off-Road Intelligent Vehicle

Off-road intelligent vehicle is an important application about Internet of Vehicles technology used in the transportation field, and the front obstacle recognition method is the key technology for off-road intelligent vehicle. In this paper, based on smart data aggregation inspired paradigm of IoT applications, we mainly study perception technology in vehicle networking by using image data and one symmetrical speeded-up robust features detector (SURF). By considering symmetry and image data aggregation, we found that data aggregation had the ability of providing global information for Internet of Vehicles systems. After we have built the experiment platform, the experiment results showed that this method is faster than Scale-Invariant Feature Transform algorithm in this case, which can satisfy the water detection accuracy and the real-time requirement. So, this method is effective for the water images detection with great symmetry to off-road intelligent vehicle, and it also gives a useful reference about environment perception technology and smart data aggregation inspired paradigm used in future Internet of Vehicles, intelligent vehicle, and traffic safety applications.

Probabilistic field approach for motorway driving risk assessment

We present an approach to assess the risk taken by on-road vehicles within the framework of artificial field theory, envisioned for safety analysis and design of driving support/automation applications. Here, any obstacle (neighboring entity on the road) to the subject vehicle is treated as a finite scalar risk field that is formulated in the predicted configuration space of the subject vehicle. The driving risk estimate is the strength of the risk field at the subject vehicle’s future location. This risk field is formulated as the product of two factors: collision probability and expected crash energy. The collision probability with neighboring vehicles is estimated based on probabilistic motion predictions. The risk can be assessed for a single time step or over multiple future time steps, depending on the required temporal resolution of the estimates. We verified the single step approach in three near-crash situations from a naturalistic dataset and in cut-in and hard-braking scenarios with simulation and showed the application of the multi-step approach in selecting the safest path in a lane-drop section. The risk descriptions from the proposed approach qualitatively reflect the narration of the situation and are in general consistent with Time To Collision. Compared to current surrogate measures of safety, the proposed risk estimate provides a better basis to assess the driving safety of an individual vehicle by considering the uncertainty over the future ambient traffic state and magnitude of expected crash consequences. The proposed driving risk model can be used as a component of intelligent vehicle safety applications and as a comprehensive surrogate measure for assessing traffic safety.

An improved rank criterion-based NLOS node detection mechanism in VANETs

PurposeAn Improved Rank Criterion-based NLOS node Detection Mechanism (IRC-NLOS-DM) is proposed based on the benefits of a reputation model for effective localization of NLOS nodes during the dynamic exchange of emergency messages in critical situations.Design/methodology/approachThis proposed IRC-NLOS-DM scheme derives the benefits of a reputation model that influentially localizes the NLOS nodes under dynamic exchange of emergency messages. This proposed IRC-NLOS-DM scheme is an attempt to resolve the issues with the routing protocols that aids in warning message delivery of vehicles that are facing NLOS situations with the influence of channel contention and broadcast storm. It is developed for increasing the warning packet delivery rate with minimized overhead, delay and channel utilization.FindingsThe simulation results of the proposed IRC-NLOS-DM scheme confirmed the excellence of the proposed IRC-NLOS-DM over the existing works investigated based on the channel utilization rate, neighborhood prediction rate and emergency message forwarding rate.Practical implicationsIt is proposed for reliable warning message delivery in Vehicular Ad hoc Networks (VANETs) which is referred as the specialized category of mobile ad hoc network application that influences Intelligent Transportation Systems (ITS) and wireless communications. It is proposed for implementing vehicle safety applications for constructing a least cluttered and a secure environment on the road.Originality/valueIt is contributed as a significant mechanism for facilitating reliable dissemination of emergency messages between the vehicular nodes, which is essential in the critical environment to facilitate a risk-free environment. It also aids in creating a reliable environment for accurate localization of Non-Line of Sight (NLOS) nodes that intentionally introduces the issues of broadcasting storm and channel congestion during the process of emergency message exchanges.

Traffic Flow Density Model and Dynamic Traffic Congestion Model Simulation Based on Practice Case with Vehicle Network and System Traffic Intelligent Communication

The massive increase in the number of vehicles has set a precedent in terms of congestion, being one of the important factors affecting the flow of traffic, but there are also effects on the world economy. The studies carried out so far try to highlight solutions that will streamline the traffic, as society revolves around transportation and its symmetry. Current research highlights that the increased density of vehicles could be remedied by dedicated short-range communications (DSRC) systems through communications of the type vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) or vehicle-to-everything (V2X). We can say that wireless communication technologies have the potential to significantly change the efficiency and road safety, thus improving the efficiency of transport systems. An important factor is to comply with the requirements imposed on the use of vehicle safety and transport applications. Therefore, this paper focuses on several simulations on the basis of symmetry models, implemented in practical cases in order to streamline vehicle density and reduce traffic congestion. The scenarios aim at both the communication of the vehicles with each other and their prioritization by the infrastructure, so we can have a report on the efficiency of the proposed models.

Design and Intensive Experimental Evaluation of an Enhanced Visible Light Communication System for Automotive Applications.

As the interest toward communication-based vehicle safety applications is increasing, the development of secure wireless communication techniques has become an important research area. In this context, the article addresses issues that are related to the use of the visible light communication (VLC) technology in vehicular applications. Thus, it provides an extensive presentation concerning the main challenges and issues that are associated to vehicular VLC applications and of some of the existing VLC solutions. Moreover, the article presents the aspects related to the design and intensive experimental evaluation of a new automotive VLC system. The experimental evaluation performed in indoor and outdoor conditions shows that the proposed system can achieve communication distances up to 50 m and bit error ratio (BER) lower than 10−6, while being exposed to optical and weather perturbations. This article provides important evidence concerning the snowfall effect on middle to long range outdoor VLC, as the proposed VLC system was also evaluated in snowfall conditions. Accordingly, the experimental evaluation showed that snowfall and heavy gust could increase bit error rate by up to 10,000 times. Even so, this article provides encouraging evidence that VLC systems will soon be able to reliably support V2X communications.

Improved Candidate Generation for Pedestrian Detection using Background Modeling in Connected Vehicles

Pedestrian detection is widely used in today’s ve-hicle safety applications to avoid vehicle-pedestrian accidents. The current technology of pedestrian detection utilizes onboard sensors such as cameras, radars, and Lidars to detect pedestrians, then information is used in a safety feature like Automatic Emer-gency Braking (AEB). This paper proposes pedestrian detection system using vehicle connectivity, image processing and computer vision algorithms. In the proposed model, vehicles collect image frames using on-vehicle cameras, then frames are transferred to the Infrastructure database using Vehicle to Infrastructure communication (V2I). Image processing and machine learning algorithms are used to process the infrastructure images for pedestrian detection. Background modeling is used to extract the foreground regions in an image to identify regions of interest for candidate generation. This paper explains the algorithms of the infrastructure pedestrian detection system, which includes image registration, background modeling, image filtering, candi-date generation, feature extraction, and classification. The paper explains the MATLAB implementation of the algorithm with a road-collected dataset and provides analysis for the detection results with respect to detection accuracy and runtime. The algorithm implementation results show an improvement in the detection performance and algorithm runtime.

Cache Agent Based Location Aided Routing Protocol Using Direction for Performance Enhancement in VANET

Vehicular Ad-hoc Networks (VANET) is a vital technology for futuristic Intelligent Transport System (ITS). VANET is a sub level of Mobile Ad-hoc Network (MANET), which establishes connectivity among vehicles with wireless medium. Once the vehicles communicate directly with each other, then there arises a new era for vehicle and road safety applications. At the same time creates new challenges by mobility of vehicles with dynamic network environment. As ITS keeps developing, there is a necessity to enhance safety application with high packet delivery rates, and reduce packet latency. This paper proposes a hybrid routing protocol called Directional-Cache Agent based Location Aided Routing (D-CALAR). D-CALAR is a combination of position based and Geocast routing. A mathematical representation for D-CALAR protocol is derived to analyze its performance. Simulation has been carried out using NS-2.33 and VanetMobiSim. From the simulated result, it seems that D-CALAR routing protocol outperformed DD-LAR, PSCAR and ID-LAR in terms of Packet Delivery Ratio, Re-transmission Ratio, Average Delay and Hop Count.