Basic Safety Messages Research Articles

Improving Information Age in SAE J2945 Congestion-Controlled Beaconing

The Society of Automotive Engineers J2945 standard prescribes a wireless channel congestion control algorithm for basic safety messages in vehicular safety communication. The algorithm utilizes transmit (Tx) power and rate controls, but we find that the Tx power control hardly engages in most practical vehicular traffic densities. We explain the cause and propose an alternative way to utilize both the control mechanisms. Specifically, we suggest that the Tx power control be first exerted to its limit, followed by the Tx rate control coping with further congestion. We show that the proposed alternative not only addresses the inadequate power control issue but also leads to better information age performance for vehicles in the most safety-critical distances.

A Bus-Oriented Mobile FCNs Infrastructure and Intra-Cluster BSM Transmission Mechanism

With the concept of edge computing being put forward, the edge node should play an important role in resource allocation and local computation. However, there are two basic problems to confront in this arena: the performance of basic safety message (BSM) exchange and the frequency of service migration, both of which are related to the vehicle’s mobility feature. To decrease the frequency of service migration and enhance the efficiency of the BSM exchange, in this paper, a bus-oriented mobile fog computing nodes infrastructure is proposed, while a mobile similarity-based clustering method is given. In the proposed method, the bus node, which is considered as a fog-computing node (FCN), is used as a sink node, which fulfills communication resource allocation and convergence computing tasks. MATLAB is used to simulate the path loss feature, while a network simulator (NS-3) is employed to investigate the performance of the proposed scheme. The simulation results show that the proposed scheme significantly decreases the frequency of service migration and perform high communication efficiency for intra-cluster BSMs exchange.

ASTSMAC: Application Suitable Time-Slot Sharing MAC Protocol for Vehicular Ad Hoc Networks

Vehicles and roadside units can be connected by a vehicular ad hoc network (VANET), which is an important component of future intelligent transport systems that can support various safety and non-safety related services. The network topology changes rapidly in VANET due to the high mobility of vehicles, making it difficult to design a reliable and efficient medium access control (MAC) protocol. Many MAC protocols based on time-division multiple access (TDMA) have been proposed for VANET to optimize network performance, but few of them consider the different requirements of nodes. Nodes may have diverse requirements for spectrum access, and one node may have a diversified strategy for transmitting messages over time, as its motion and network topology change rapidly. The application layer should dynamically adjust the package generation frequency as needed to reduce unnecessary spectrum access. In this paper, we propose a novel application suitable time slot sharing MAC protocol (ASTSMAC) for the broadcasting of the basic safety messages (BSMs) that can adapt to different transmission cycle requirements in the application layer program. In our scheme, one slot can be shared by more than one node, even if they are in each other’s communication range. Simulation results show that ASTSMAC can provide significantly higher packet delivery ratio and lower collision rate. Further, the network can accommodate more nodes when the traffic-density is high.

Pseudonym Management Through Blockchain: Cost-Efficient Privacy Preservation on Intelligent Transportation Systems

Research into the established area of the intelligent transportation system is evolving into the Internet of Vehicles, a fast-moving research area, fuelled in part by rapid changes based on cyber-physical systems. It needs to be recognized that existing vehicular communication systems are susceptible to privacy vulnerabilities which require addressing. A practical challenge is that many vehicular communication applications and services make use of basic safety messages that contain the identity of the vehicle, location, and other personal data. A popular way of dealing with this privacy issue is to utilize a pseudonym change scheme to protect the vehicle's identity and location. However, many such schemes suffer that the cost grows and the certificate management difficulty raises with the number of pseudonyms generated and stored, casting doubt of the economic feasibility of that approach. We propose a decentralized blockchain-based solution for pseudonym management that overcomes these limitations. This scheme consists of pseudonym distribution and a shuffle operation, allowing the reuse of existing pseudonyms to different vehicles. The results reported here, including those from our simulations, demonstrate that the proposed scheme can reuse existing pseudonyms and achieve a better degree of anonymity at a lower cost than existing schemes.

Design of the Emergency Messages Integration for Vehicle to Everything (V2X) Communications of Connected Cars

So far, international standardization of message specifications between V2X(Vehicle to Everything) safety communications are BSM(Basic Safety Message) by US-led SAE and CAM(Cooperative Awareness Message)/DENM(Decentralized Environmental Notification Message) by EU-led ETSI. However, in the emergency situation such as car accidents or road traffic jam, there may be insufficient time to process all messages. In this paper, we propose an integration of emergency messages for V2X communications of connected vehicles in VANETs(Vehicular Ad hoc Networks), To do this, we are using the Mobile Virtual Fence(MVF), which has been preliminarily researched and developed. So we extract and integrate only emergency messages as the fields of similar messages between BSM and CAM/DENM, and apply it to the MVF. If it will be the connected MVFs like connected cars in the future and any traffic accident or traffic jam occurs, the proposed messages will be communicated to each other to avoid the accident. Therefore the proposed integration of emergency messages between BSM and CAM/DENM specification will be contribute to improve the safety and reliability as the unified and integrated emergency messages.

Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)

Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message.

How is driving volatility related to intersection safety? A Bayesian heterogeneity-based analysis of instrumented vehicles data

Driving behavior in general is considered a leading cause of intersection related traffic crashes. However, due to unavailability of real-world driving data, intersection safety performance evaluations are largely reactive where state-of-the-art methods are applied to analyze historical crash data. In this regard, the emerging connected vehicles technology provides a promising opportunity for investigating intersection safety more from a proactive perspective. Driving volatility captures the extent of variations in instantaneous driving decisions when a vehicle is being driven. This study develops a fundamental understanding of microscopic driving volatility and how it relates to unsafe outcomes at intersections. Using high resolution driving data from a real-world connected vehicle testbed, Safety Pilot Model Deployment, in Ann Arbor, Michigan, a methodology is presented to quantify driving volatility at 116 intersections by analyzing more than 230 million real-world Basic Safety Messages. For proactive intersection safety evaluation, the large-scale connected vehicle data is then linked to detailed intersection data containing crashes, traffic exposure, and other geometric features. By using vehicular speed, acceleration/deceleration, and vehicular jerk based eight different volatility measures, descriptive analysis is performed to spot differences between driving volatility at signalized and un-signalized intersections. Then, in-depth statistical analysis is conducted separately for all intersections (signalized and un-signalized) and signalized intersections only. Importantly, not all factors that may influence crash frequency can be observed in the data. If unobserved factors could be included in a model, then correlations between driving volatility and crash frequency can change, e.g., the relationship can become statistically insignificant. Given the important methodological concerns of unobserved heterogeneity and potential omitted variable bias, hierarchical fixed- and random-parameter Poisson and Poisson log-normal models are estimated. Full Bayesian estimation via Markov Chain Monte Carlo (MCMC) based Gibbs sampling is performed, providing more efficient results. For all intersections, after controlling for traffic exposure, geometrics, and unobserved factors, a one-percent increase in intersection-level volatility calculated through two standard deviations threshold for acceleration/deceleration, passing level volatility captured through coefficient of variation of speed, and mean absolute deviance of vehicular jerk results in a 1.25%, 0.25%, and 0.35% increase in crash frequencies respectively. However, the relationships between intersection-specific volatility and crash frequencies are different for signalized intersections. Several of the exogenous factors are found to be normally distributed random parameters, suggesting that the effects of such variables vary across different intersections. The implications of the findings for proactive safety management are discussed.

Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.

Application of Real Field Connected Vehicle Data for Aggressive Driving Identification on Horizontal Curves

The emerging technology of connected vehicles generates a vast amount of data that could be used to enhance roadway safety. In this paper, we focused on safety applications of a real field connected vehicle data on a horizontal curve. The database contains connected vehicle data that were collected on public roads in Ann Arbor, Michigan with instrumented vehicles. Horizontal curve negotiations are associated with a great number of accidents, which are mainly attributed to driving errors. Aggressive/risky driving is a contributing factor to the high rate of crashes on horizontal curves. Using basic safety message data in connected vehicle data set, this paper modeled aggressive/risky driving while negotiating a horizontal curve. The model was developed using the machine learning method of random forest to classify the value of time to lane crossing (TLC), a proxy for aggressive/risky driving, based on a set of motion-related metrics as features. Three scenarios were investigated considering different TLCs value for tagging aggressive driving moments. The model contributed to high detection accuracy in all three scenarios. This suggests that the motion-related variables used in the random forest model can accurately reflect drivers’ instantaneous decisions and identify their aggressive driving behavior. The results of this paper inform the design of warning/feedback systems and control assistance from unsafe events which are transmittable through vehicles-to-vehicles and vehicles-to-infrastructure applications.

Extracting Useful Information from Basic Safety Message Data: An Empirical Study of Driving Volatility Measures and Crash Frequency at Intersections

With the emergence of high-frequency connected and automated vehicle data, analysts can extract useful information from them. To this end, the concept of “driving volatility” is defined and explored as deviation from the norm. Several measures of dispersion and variation can be computed in different ways using vehicles’ instantaneous speed, acceleration, and jerk observed at intersections. This study explores different measures of volatility, representing newly available surrogate measures of safety, by combining data from the Michigan Safety Pilot Deployment of connected vehicles with crash and inventory data at several intersections. For each intersection, 37 different measures of volatility were calculated. These volatilities were then used to explain crash frequencies at intersection by estimating fixed and random parameter Poisson regression models. Given that volatility reflects the degree to which vehicles move, erratic movements are expected to increase crash risk. Results show that an increase in three measures of driving volatility are positively associated with higher intersection crash frequency, controlling for exposure variables and geometric features. More intersection crashes were associated with higher percentages of vehicle data points (speed & acceleration) lying beyond threshold-bands. These bands were created using mean plus two standard deviations. Furthermore, a higher magnitude of time-varying stochastic volatility of vehicle speeds when they pass through the intersection is associated with higher crash frequencies. These measures can be used to locate intersections with high driving volatilities. A deeper analysis of these intersections can be undertaken, and proactive safety countermeasures considered to enhance safety.

Vanets Meet Autonomous Vehicles: Multimodal Surrounding Recognition Using Manifold Alignment

In the past two years, calls for developing synergistic links between the two worlds of vehicular ad-hoc networks (VANETs) and autonomous vehicles have significantly gone up to achieve further on-road safety and benefits for end-users. In this paper, we present our vision to create such a beneficial link by designing a multimodal scheme for object detection, recognition, and mapping based on the fusion of stereo camera frames, point cloud Velodyne LIDAR scans, and vehicle-to-vehicle (V2V) basic safety messages (BSMs) exchanges using VANET protocols. Exploiting the high similarities in the underlying manifold properties of the three data sets, and their high neighborhood correlation, the proposed scheme employs semi-supervised manifold alignment to merge the key features of rich texture descriptions of objects from 2-D images, depth and distance between objects provided by 3-D point cloud, and the awareness of self-declared vehicles from BSMs’ 3-D information including the ones not seen by camera and LIDAR. The proposed scheme is applied to create joint pixel-to-point-cloud and pixel-to-V2V correspondences of objects in frames from the KITTI Vision Benchmark Suite, using a semi-supervised manifold alignment, to achieve camera-LIDAR and camera-V2V mapping of their recognized objects. We present the alignment accuracy results over two different driving sequences and show the additional acquired knowledge of objects from the various input modalities. We also study the effect of the number of neighbors employed in the alignment process on the alignment accuracy. With proper choice of parameters, the testing of our proposed scheme over two entire driving sequences exhibits 100% accuracy in the majority of cases, 74%–92% and 50%–72% average alignment accuracy for vehicles and pedestrians and up to 150% additional object recognition of the testing vehicle’s surrounding.

A broadcast scheme for vehicle-to-pedestrian safety message dissemination

Ensuring cooperative awareness by periodic message beaconing in vehicular environments is necessary to address pedestrian safety. However, high periodic basic safety message broadcasting in dense vehicular environments makes accessing the communication channel very competitive. Furthermore, high-frequency periodic broadcasting causes fast device energy dissipation which is a key issue for small computing devices used in wireless sensor and mobile communications. Therefore, in order to achieve reliable message dissemination for vehicle-to-pedestrian safety, energy loss minimization mechanisms for pedestrian mobile devices should be developed. This article proposes controlling the number of broadcasts by eliminating periodic safety message broadcasts from pedestrian nodes; these nodes only receive broadcasts from vehicles and then conditionally communicate with the vehicles when safety alerts are raised. When the pedestrian nodes do not receive messages from any vehicle for a specified period, pedestrian no...

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N=714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.

Jamming Attack Model and Detection Method for Beacons Under Multichannel Operation in Vehicular Networks

Safety applications in vehicular networks involve real-time information included in periodical exchanged messages, called beacons. Denial of Service (DoS) attacks, especially jamming, may have high impact on these basic safety messages mainly because of the adequate condition of the attack at the lower layers. Due to the important role of these safety messages, securing beacons against jamming attacks is primordial. First, we investigate the impact of jamming attacks on broadcasting by introducing a new analytical model. We show that the degradation at certain levels in network performance is an indication of a jamming attack in the network; therefore, results from this analysis will allow us to determine network performance thresholds to distinguish normal operation and attacks. Second, we study the feasibility of the existing threshold-based methods to detect jamming in real-time applications. From our analysis, these methods are not suitable for safety applications. Hence, we propose a real-time Medium-Access-Control-based (MAC-based) detection method detection method to meet the requirements of safety applications in vehicular networks. Our detection method can more accurately distinguish the causes of failed transmissions, such as contention collisions, interferences, and jamming attacks. The jamming attacks are detected with low probability of false alarms. Finally, we evaluate our detection method with both analytical analysis and network simulations. Our detection method performs high detection probability in different studied scenarios while false alarms are almost avoided.

Development of potential methods for testing congestion control algorithm implemented in vehicle-to-vehicle communications

ABSTRACTObjective: A channel congestion problem might occur when the traffic density increases because the number of basic safety messages carried on the communication channel also increases in vehicle-to-vehicle communications. A remedy algorithm proposed in SAE J2945/1 is designed to address the channel congestion issue by decreasing transmission frequency and radiated power. This study is to develop potential test procedures for evaluating or validating the congestion control algorithm.Methods: Simulations of a reference unit transmitting at a higher frequency are implemented to emulate a number of onboard equipment (OBE) transmitting at the normal interval of 100 ms (10 Hz). When the transmitting interval is reduced to 1.25 ms (800 Hz), the reference unit emulates 80 vehicles transmitting at 10 Hz. By increasing the number of reference units transmitting at 800 Hz in the simulations, the corresponding channel busy percentages are obtained. An algorithm for Global Positioning System (GPS) data generation of virtual vehicles is developed for facilitating the validation of transmission intervals in the congestion control algorithm.Results: Channel busy percentage is the channel busy time over a specified period of time. Three or 4 reference units are needed to generate channel busy percentages between 50 and 80%, and 5 reference units can generate channel busy percentages above 80%. The proposed test procedures can verify the operation of congestion control algorithm when channel busy percentages are between 50 and 80% and above 80%. By using a GPS data generation algorithm, the test procedures can also verify the transmission intervals when traffic densities are 80 and 200 vehicles in a radius of 100 m. A suite of test tools with functional requirements is also proposed for facilitating the implementation of test procedures.Conclusions: The potential test procedures for a congestion control algorithm are developed based on the simulation results of channel busy percentage and the GPS data generation algorithm. The test tools can examine the requirement compliance automatically and objectively. The required number of reference units should be further validated using real OBEs before implementing these potential test procedures.

Adaptive security provisioning for vehicular safety applications

Vehicular ad hoc network provides safety applications for next generation intelligent transport systems. By periodically transmitting mobility information in basic safety messages (BSMs), vehicles get an overview of the neighbourhood. As applications involving vehicular networks impact human safety, reliability of BSMs is a key requirement, which however is a challenging task in heavy traffic scenarios where many BSMs are queued up simultaneously for signature verification. This results in long verification delays for many critical BSMs from nearby vehicles. To overcome this challenge, we propose two adaptive security mechanisms in this paper that can be used by the ITS applications to enhance their QoS and maintain good level of security. The first technique is a receiver-oriented technique that uses channel aware mechanism to prioritise the signature verification of BSMs from closer neighbours. The second technique is transmitter based that can adaptively select the best security level for BSMs according to cryptographic loss rate. Simulation results verify the performance enhancement achieved by the proposed framework in terms of several safety awareness metrics as compared with the existing schemes.

Enhancing Relative Positioning Accuracy in Vehicle-to-Vehicle Safety: Multivehicle Kinematics and Nonlinear Particle Filtering Approach

Unsatisfactory relative positioning accuracy is a bottleneck in collision avoidance applications of vehicle-to-vehicle communication. Previous efforts focused mainly on either enhancing absolute GPS data precision or experimenting with positioning techniques alternative to GPS, such as real-time kinematics, Doppler measurements, or integration of GPS with ultrawideband ranging radios. This paper proposes a new approach that is independent from the sources and precisions of raw data. Based on a multivehicle kinematics model adapted from a multirobot domain, the method can predict vehicles’ distances and bearings by fully utilizing the shared real-time motion data from dedicated short-range communications’ basic safety message packets. Three types of nonlinear filters were proposed and compared to minimize the errors caused by raw data noise and system timing inaccuracies. Experiments indicated that the extended Kalman filter failed to estimate a highly nonlinear process, unscented Kalman filter dysfunctions, if the statistical features of errors were misestimated; only the particle filter maintained satisfactory and stable accuracies in both high-nonlinearity and error-unknown situations. The combination of the kinematics model and particle filtering can serve as an algorithmic complement to various approaches focused on data sources to enhance relative positioning precision.

Can Data Generated by Connected Vehicles Enhance Safety?: Proactive Approach to Intersection Safety Management

Traditionally, evaluation of intersection safety has been largely reactive and based on historical crash frequency data. However, the emerging data from connected and autonomous vehicles can complement historical data and help in proactively identifying intersections with high levels of variability in instantaneous driving behaviors before the occurrence of crashes. On the basis of data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, this study developed a unique database that integrated intersection crash and inventory data with more than 65 million real-world basic safety messages logged by 3,000 connected vehicles; this database provided a more complete picture of operations and safety performance at intersections. As a proactive safety measure and a leading indicator of safety, location-based volatility was introduced; this quantified variability in instantaneous driving decisions at intersections. Location-based volatility represented the driving performance of connected-vehicle drivers traveling through a specific intersection. As such, with the use of the coefficient of variation as a standardized measure of relative dispersion, location-based volatility was calculated for 116 intersections in Ann Arbor. Rigorous fixed- and random-parameter Poisson regression models were estimated to quantify relationships between intersection-specific volatilities and crash frequencies. Although exposure-related factors were controlled for, the results provided evidence of a statistically significant (at the 5% level) positive association between intersection-specific volatility and crash frequencies for signalized intersections. The implications of these findings for proactive intersection safety management are discussed.

A method for connected vehicle trajectory prediction and collision warning algorithm based on V2V communication

ABSTRACTConnected vehicle communication technology is rapidly developing in recent years, and host vehicle (HV) can send or receive the basic safety message (BSM) from the remote vehicles (RVs). However, there are few applications using this information to improve the driving safety. In this paper, we propose a collision warning predicted framework that provides connected automated vehicle and alert driver when time to collision (TTC) is within specified thresholds. After pre-processor RV BSM data, this paper transforms the RV position and calculates the relative position, distance and speed. Then, the RV trajectory is estimated by using Kalman filter algorithm, and the error statistics of the prediction of latitude and longitude are analysed. The prediction results show that it works on straight, corner and the curve road, but the latitude error is higher than the longitude error. At last this paper constructs the relative position radar map for the HV, in which it can show the relative position, speed and TTC information. Vehicle collision can be detected in real time and the vehicle can prevent the potential conflict accordingly by using these information.

Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles

When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10Hz. BSMs describe a vehicle’s position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.