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Vehicles can easily lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. In addition, through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space, a friction value, and a confidence interval within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface, where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs. 

The development of communication technologies in edge computing has fostered progress across various applications, particularly those involving vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Enhanced infrastructure has improved data transmission network availability, promoting better connectivity and data collection from IoT devices. A notable IoT application is with the Intelligent Transportation System (ITS). IoT technology integration enables ITS to access a variety of data sources, including those pertaining to weather and road conditions. Real-time data on factors like temperature, humidity, precipitation, and friction contribute to improved decision-making models. Traditionally, these models are trained at the cloud level, which can lead to communication and computational delays. However, substantial advancements in cloud-to-edge computing have decreased communication relays and increased computational distribution, resulting in faster response times. Despite these benefits, the developments still largely depend on central cloud sources for computation due to restrictions in computational and storage capacity at the edge. This reliance leads to duplicated data transfers between edge servers and cloud application servers. Additionally, edge computing is further complicated by data models predominantly based on data heuristics. In this paper, we propose a system that streamlines edge computing by allowing computation at the edge, thus reducing latency in responding to requests across distributed networks. Our system is also designed to facilitate quick updates of predictions, ensuring vehicles receive more pertinent safety-critical model predictions. We will demonstrate the construction of our system for V2V and V2I applications, incorporating cloud-ware, middleware, and vehicle-ware levels. 

Vehicles are highly likely to lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. And through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space and a friction value within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs. By defining a cloud-based data sharing method for the networks of CAVs, this road friction mapping strategy provides great potential for improving CAVs' control performance and stability via database-mediated feedback systems. 

Autonomous Vehicles (AVs) are an emerging and highly impactful technology on today's roads. When assessing the performance of AVs, it is useful to study their improvement relative to common metrics such as fuel economy/emissions, safety, and congestion. But metrics of the vehicle's performance alone may not be complete; an AV that is affecting and reacting to a smart traffic light, for example, may improve its own performance, but may cause the same intersection to degrade the performance of other vehicles around the AV. Similar concerns arise in nearly all AV topics: platooning, light pre-emption, lane tracking, etc. Thus, the assessment of the vehicle's impacts on surrounding traffic is important, possibly even more important than the improvements enabled on the AV alone. But what boundary, or factors, define the vehicles, equipment, etc. “surrounding” an AV? The goal of this work is to characterize the boundary of vehicles “surrounding” an AV, referred to as Region of Influence, or ROI. Specifically, this work focuses on the problem that considering a perturbation is exerted into a traffic system, how far in time and space the perturbation from an AV’s decision can influence the surrounding system’s behavior. To achieve the goal, we utilized AIMSUN, a microscopic traffic simulator, to perform baseline and perturbed simulations. The ROI was evaluated by comparing trajectories of traffic surrounding the ego vehicle using different metrics, including difference in trajectories, Euclidian distance, rate of change of Euclidian distance, total number of lane changes over the whole simulation space versus time and total number of lane changes over the whole simulation time versus distance to ego vehicle. The results show that the ROI can be viewed from different perspectives using these metrics, and it is dependent on speed variance of the traffic. 

Due to the lack of information, current vehicle control systems generally assume that the road friction conditions ahead of a vehicle are unchanged relative to those at the vehicle's current position. This can result in dangerous situations if the friction is suddenly decreasing from the current situation, or overly conservative driving styles if the friction of the current situation is worse than the roadway ahead. However, with connectivity either to other vehicles, infrastructure, or cloud services, future vehicles may have access to upcoming roadway information; this is particularly valuable for planning velocity trajectories that consider the friction and geometry in the road path ahead. This paper introduces a method for planning longitudinal speed profiles for Connected and Autonomous Vehicles (CAVs) that have previewed information about path geometry and friction conditions. The novelty of this approach is to explicitly include consideration of the friction ellipse available along the intended path. The paper derives an analytical solution for certain preview cases that upper-bounds the allowable vehicle velocity profile while preventing departure from the friction ellipse. The results further define the relationship between a lower bound on friction, the path geometry, and minimum friction preview distance. This relationship is used to ensure the vehicle has sufficient time to take action for upcoming hazardous situations. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a curving road with changing friction conditions, with results showing that, with sufficient preview, the vehicle could anticipate allowable and stable path keeping speed.