Basic Safety Messages Research Articles

Latency Analysis of Drone-Assisted C-V2X Communications for Basic Safety and Co-Operative Perception Messages

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability.

Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety

Cooperative perception that integrates sensing capabilities from both infrastructure and vehicle perception sensors can greatly benefit the transportation system with respect to safety and data acquisition. In this study, we conduct a preliminary evaluation of such a system by integrating a portable lidar-based infrastructure detection system (namely, Traffic Scanner [TScan]) with a Society of Automotive Engineers (SAE) Level 4 connected and automated vehicle (CAV). Vehicle-to-everything (V2X) communication devices are installed on both the TScan and the CAV to enable real-time message transmission of detection results in the form of SAE J2735 basic safety messages. We validate the concept using a case study, which aims at improving CAV situation awareness and protecting vulnerable road user (VRU) safety. Field testing results demonstrate the safety benefits of cooperative perception from infrastructure sensors in detecting occluded VRUs and helping CAVs to plan safer (i.e., higher post-encroachment time) and smoother (i.e., lower deceleration rates) trajectories.

Verifying Certificate Revocation Status for Short Key Lengths in Vehicle Communication Systems

This paper proposes and analyzes a ticket-based OCSP protocol for efficient certificate revocation checking in vehicle communication systems. The IEEE WAVE standard for vehicular networks requires real-time processing of Basic Safety Messages (BSMs) exchanged between vehicles. Traditional OCSP revocation checking can introduce delays. The proposed approach distributes OCSP responses as tickets valid for a road section. Vehicles use shorter keys extracted from the tickets for faster cryptographic processing. Experiments compare processing times for signature generation and verification with different elliptic curves. The results show the proposed technique provides faster revocation checking. This allows time-critical vehicle-to-vehicle message processing at high rates under computational constraints. The ticket-based OCSP mechanism enhances security while meeting real-time requirements in vehicular networks.

Novel Intelligent BSM Falsification Attack Detection System Using Trusted Neighbor Vehicle Approach in IoV

The proliferation of cyberattacks has emerged as a significant obstacle for advancing technologies such as the Internet of Things (IoT) and Internet of Vehicles (IoV) in recent times. Notably, cryptographic security measures have been implemented in IoV to counteract these cyberattacks. However, these security measures are inadequate when it comes to thwarting internal attackers within the network, as these attackers possess the necessary security credentials for authenticating basic safety messages (BSMs). The research community has made substantial contributions by proposing misbehavior detection systems (MDS) based on data-centric machine learning to identify and prevent internal attackers within IoV. Nevertheless, the existing MDSs in the literature rely on BSMs received from a single vehicle, thereby enabling internal attackers to manipulate their falsified BSMs and evade detection, resulting in a high incidence of false alarms. In this study, we introduce a new intelligent system for detecting falsified BSMs, employing a trusted neighbor vehicle approach (NIBFADS-UTVA)). Our approach demonstrates exceptional effectiveness, achieving an accuracy, precision, recall, and F1-Score all exceeding 99%.

A Study on Reducing Traffic Congestion in the Roadside Unit for Autonomous Vehicles Using BSM and PVD

With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow.

Broadcast and Silence Period (BSP): A Pseudonym Change Strategy

Vehicles in Internet of Vehicles must constantly broadcast basic safety messages to keep other vehicles informed of their location, speed, and other parameters. Attackers can use the content of these messages to follow the path of the vehicle and invade vehicle owner privacy. Vehicle location privacy was ensured by broadcasting these messages using pseudonyms and periodically changing these pseudonyms. However, in mix-zone-based strategies, vehicles are frequently restricted from changing their pseudonyms in specific places. Furthermore, most previous change strategies only safeguarded against the syntactic linking attack and did not account for the semantic linking attack. Thus, in this study, we proposed a broadcast and silence period pseudonym change strategy to overcome these difficulties. Mix zones were obtained by analyzing the locations where vehicles frequently stop. The vehicle broadcasts or silences in a mix zone according to our broadcast and silence period strategy and changes to the pseudonym assigned by the road side unit during the silence phase until it leaves the zone. Our safety analysis and experimental results indicate that the broadcast and silence period prevent both linking attacks while reducing the impact of radio silence on vehicle safety by limiting the silent time, thereby providing better location privacy protection for vehicles compared with the existing schemes. In addition, the number of signatures required to be verified each time it receives messages and the overhead incurred in changing pseudonyms for vehicles is acceptable.

Faster verification of V2X basic safety messages via Message Chaining

Vehicular-to-Everything (V2X) communications enable vehicles to exchange messages with other entities, including nearby vehicles and pedestrians. V2X is, thus, essential for establishing an Intelligent Transportation System (ITS), where vehicles use information from their surroundings to reduce traffic congestion and improve safety. To avoid abuse, V2X messages should be digitally signed using valid digital certificates. Messages sent by unauthorized entities can then be discarded, while misbehavior can lead to the revocation of the corresponding certificates. One challenge in this scenario is that messages must be verified shortly after arrival (e.g., within centiseconds), whereas vehicles may receive thousands of them per second. To handle this issue, some solutions propose prioritization or delayed-verification mechanisms, while others involve signature schemes that support batch verification. In this manuscript, we discuss two mechanisms, one based on Message Authentication Codes, and one based on hash chaining, that complement such proposals, enabling the authentication of a sequence of messages from the same source with one single signature verification. Our analysis shows that the technique can reduce the number of verified signatures and computational costs by around 90% for reliable communication channels, and by more than 65% for a maximum packet loss rate of 20%.

Vehicle Environment Awareness-Based Messages Transmission Frequency Optimization in C-V2X

Road accidents can be effectively reduced by broadcasting basic safety messages (BSMs) of vehicles in Cellular V2X (C-V2X). However, with the increase of the number of C-V2X devices, the collision probability of BSMs at a fixed transmission frequency increases due to limited spectrum resources. In this paper, the transmission frequency adjustment mechanism (TFAM) based on channel busy ratio (CBR) is proposed to maintain the timeliness of BSMs. First, the CBR is estimated based on BSM transmission frequency and sensing occupancy probability considering channel fading. To quantify the performance of C-V2X communication, the performance metric freshness of information (FOI) is defined, which comprehensively trades off the reliability and latency of BSMs. Furthermore, an optimization problem is formulated to minimize FOI. We solve the optimization problem to derive the optimal transmission frequency. Simulation results show that the proposed TFAM enables vehicles to exchange BSMs with minimum FOI.

Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles.

The Internet of vehicles (IoVs) is an innovative paradigm which ensures a safe journey by communicating with other vehicles. It involves a basic safety message (BSM) that contains sensitive information in a plain text that can be subverted by an adversary. To reduce such attacks, a pool of pseudonyms is allotted which are changed regularly in different zones or contexts. In base schemes, the BSM is sent to neighbors just by considering their speed. However, this parameter is not enough because network topology is very dynamic and vehicles can change their route at any time. This problem increases pseudonym consumption which ultimately increases communication overhead, increases traceability and has high BSM loss. This paper presents an efficient pseudonym consumption protocol (EPCP) which considers the vehicles in the same direction, and similar estimated location. The BSM is shared only to these relevant vehicles. The performance of the purposed scheme in contrast to base schemes is validated via extensive simulations. The results prove that the proposed EPCP technique outperformed compared to its counterparts in terms of pseudonym consumption, BSM loss rate and achieved traceability.

Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards

The development of quantum computing systems poses a great threat to the security of existing public key-based systems. As a result, the National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography (PQC) standardization project in 2015, and currently active research is being conducted to apply PQC to various cryptographic protocols. Unlike elliptic curve cryptography (ECC)-based schemes, PQC requires a large memory footprint and key/signature size. Therefore, when migrating PQC to a protocol, depending on the PQC and protocol specifications, it can be hard to migrate PQC. In the case of the WAVE protocol, it is difficult to satisfy the accuracy of a specific PQC algorithm because segmentation of the signature occurs during transmission due to the limitation of the maximum packet size. Therefore, in this paper, we present two methodologies that can apply PQC while complying with IEEE 1609.2 standards to the WAVE protocol in the V2V environment. Whereas previous migration studies have focused on designing a hybrid mode of protocols, this paper explores solutions more intuitively at the application layer of protocols. We analyzed two postquantum digital signature algorithms (Crystals-Dilithium and Falcon) and the structure of basic-safety messages (BSMs) of the V2V protocol on the size side. Through this, we propose methods that can perform an independent signature verification process without waiting for all divided signatures in the WAVE protocol. Our methodology overcomes the limitation that schemes with large signature sizes cannot be mounted into the WAVE protocol. We also note that the architecture used as an on-board unit (OBU) in an autonomous driving environment is mainly a microprocessor. We investigated an optimized PQC implementation in the OBU environment and simulated our methodology with the V2Verifier. Finally, we measured the accurate latency through simulation in Jetson Xavier, which is mainly used as an OBU in the V2V communication network.

DSRC Versus LTE-V2X: Empirical Performance Analysis of Direct Vehicular Communication Technologies

Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicle-to-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP’s Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world scenarios for various road safety and traffic efficiency applications. In this paper, we present a comprehensive survey of these two technologies (i.e., DSRC and LTE-V2X) and related works. More specifically, we study the PHY and MAC layer of both technologies in the survey study and compare the PHY layer performance using a variety of field tests. First, we provide a summary of each technology and highlight the limitations of each in supporting V2X applications. Then, we examine their performance based on different metrics.

Secure Intrusion Detection by Differentially Private Federated Learning for Inter-Vehicle Networks

Along with providing several benefits, the unprecedented growth of connected and automated vehicles brings worries about damaging cyber attacks. Network-based intrusion detection systems (IDSs) using deep learning methods can effectively mitigate the threats by promptly detecting malicious behaviors. However, the centralized learning mode may cause data leverage. Federated learning has emerged as a new distributed machine learning training paradigm to preserve data privacy by allowing clients to train and validate models locally with their data and then send the model parameters to the central server. First, we propose a new framework named DPFL-F2IDS scheme for an edge inter-vehicle network that transmits Basic Safety Messages, which consists of Differentially Private Federated Learning (DPFL) and F2IDS (Framework for IDS). DPFL can defend against the member inference attacks faced by the standard federated learning, but difficulties still exist in making a tradeoff on the utility metrics and privacy metrics. Second, experiments by centralized learning methods were performed on the VeReMi Extension dataset. Third, the performance of federated learning by different numbers of vehicles and different optimizers is evaluated. DPFL by different noise values is also evaluated. Results clarified that the F1-scores reached 0.9915 and 0.9700 by long short-term memory (LSTM)-based intrusion detection for binary classification and multi-classification in the centralized learning mode, respectively. The utility results achieved by federated averaging (FedAvg) with Adabound optimizer were closer to the centralized learning mode than the classical FedAvg algorithms. The optimal values of the noise multipliers were also found without degrading the model quality and preserving privacy.

Falsification Detection System for IoV Using Randomized Search Optimization Ensemble Algorithm

Falsification detection is a critical advance in ensuring that real-time information about vehicles and their movement states is certified on the Internet of Vehicles (IoV). Thus, detecting nodes that are propagating inaccurate information is a requirement for the successful deployment of IoV services although only a few research studies have been carried out on Basic Safety Message (BSM) falsification. As such, this paper proposes a Randomized Search Optimization Ensemble-based Falsification Detection Scheme (RSO-FDS). The RSO technique was used to construct the proposed Ensemble-based Random Forest (RF) model. The evaluation was performed on three different datasets developed to evaluate falsification in IoV. In addition, the six most popular supervised learning (SL) algorithms were investigated to evaluate the capability of the proposed RSO-FDS, which had the best performance across all datasets. The performance metrics considered are computational efficiency in terms of prediction time, validation accuracy for overall attack classification, precision, recall, and F1 scores. For validation, the performance of the proposed RSO-FDS was further compared with results from recent works. Furthermore, the irrelevance of data balancing was illustrated for real-life IoV scenarios. The result shows that the proposed model outperformed state-of-the-art algorithms implemented in this work and related works.

A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET

Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users. To achieve reliable delivery of periodic status information, referred to as basic safety messages (BSMs) and event-driven alerts, vehicles need to manage the conflicting requirements of situational awareness and congestion control in a dynamic environment. To address this challenge, this paper focuses on controlling the message transmission rate through a Markov decision process (MDP) and solves it using a novel reinforcement learning (RL) algorithm. The proposed RL approach selects the most suitable transmission rate based on the current channel conditions, resulting in a balanced performance in terms of packet delivery and channel congestion, as shown by simulation results for different traffic scenarios. Additionally, the proposed approach offers increased flexibility for adaptive congestion control through the design of an appropriate reward function.

Toward a Novel RESTFUL Big Data-Based Urban Traffic Incident Data Web Service for Connected Vehicles

Abstract Connected vehicles (CVs) are an emerging technology in intelligent transportation systems. Currently, many data-driven intelligent transportation systems (D2ITS) use CV data. Unfortunately, these D2ITS still need serious improvement before they meet higher-level visualization needs. Thus, we aim to develop a new, intelligent data-driven transportation system framework. We focus on visualizing real-time CV data using a big data analytic system in urban areas. In response, we first propose an effective real-time data distribution approach within the Vehicular Ad-hoc NETwork. Second, we develop novel strategies for aggregating, extracting and ingesting data. We provide scalable and fault-tolerant delivery methods without interruption or delay. Finally, we proposed a novel visualization REpresentational State Transfer (REST) web service. We used Simulation of Urban MObility, OMNET++ and Veins to simulate a traffic incident dataset. Then, we tested the Basic Safety Messages in an experimental big data cluster. We used NIFI, Kafka and Cassandra for ingestion, distribution, delivery and storage. The results show accurate performance for packet loss, packet delivery and communication delay. Also, it indicates high throughput and low latency for distributed data delivery systems. Additionally, we obtained the smallest response time for the RESTFUL visualization web service.

A Defense System: Jamming Detection and Mitigation for Safety Applications in Vehicular Networks

The periodically exchanged basic safety messages (BSM) in vehicular networks have become a new attack target for jamming attacks that are easy to conduct in a vehicular environment. The detection and mitigation must be cordially integrated to provide acceptable communication latency under attacking conditions. This paper considers a comprehensive defense system detecting and mitigating jamming attacks. We analyze the impact of the jamming attack on BSMs on our initially proposed random channel surfing scheme coupling with a detection method. The detection method can hardly provide 100% accuracy, and this consequently delays the reaction. We study the defense system by a mathematical model which is validated by simulations in NS-3. The obtained results depict how the performance of the channel surfing scheme depends on its preinstalled detection method

MDFD: A multi-source data fusion detection framework for Sybil attack detection in VANETs

Sybil attacks in Vehicular Ad-Hoc Networks (VANETs) conduct malicious behavior by falsifying and faking messages between vehicles. It poses a significant threat to the safety of vehicle movement. Meanwhile, because Sybil attacks often hide the real identity of the attacker with the help of a legitimate pseudonym, making it very difficult to detect them. Most existing detection schemes use a single data source, which is not enough to describe the specific characteristics of the attack behavior accurately, while their detection performance is also affected by real scenario factors such as traffic flow and attacker density. Therefore, we propose a multi-source data fusion detection framework for Sybil attacks based on the study of the behavior characteristics of Sybil attacks and the impact of the attacks on the traffic flow state. We get basic safety messages data, map data and sensor data and then obtain multi-dimensional data fusion features from four aspects: spatio-temporal location relationship, traffic flow state change, vehicle behavior characteristics and sensor data verification, and finally use machine learning classification model to complete the detection of attack behavior. Experimental results show that our proposed attack detection framework is able to locate the specific road section where the attack occurred in a realistic and complex traffic scenario containing different road types without using trusted vehicles as observation nodes, and has good generalization capability. the average detection accuracy of the MDFD framework for four types of compound attacks is as high as 97.69%.

Novel hyper-tuned ensemble Random Forest algorithm for the detection of false basic safety messages in Internet of Vehicles

Detection of nodes disseminating false data is a prerequisite for effective deployment of Internet of Vehicles (IoV) services. This work proposed a novel hyper-tuned ensemble Random Forest (Ens. RF) algorithm to detect false basic safety messages in IoV. Performance evaluation was done using the Vehicular Reference Misbehavior (VeReMi) dataset comprising data-centric misbehavior evaluation for vehicular networks. For validation, a comparative analysis of the performance of the proposed “Ens. RF” model, five machine learning algorithms implemented in this work, and state-of-the-art ML models from related literature was presented. The performance metrics considered are time efficiency and validation accuracy for overall misbehavior classification. Also, the results confirmed the irrelevance of data balancing in real-life scenarios. Finally, we assess the performance of our proposed system for detecting each falsification scenario using precision and recall. The result shows that the proposed algorithm outperformed others with a validation accuracy of 99.60% and a negligible 604 misclassifications out of 153,730 points.