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Innate immunity is the body’s first line of defense for mounting robust antiviral signaling. However, the role of cytoskeletal prestress, a hallmark of cellular mechanotransduction, in regulating innate immune pathways such as retinoic acid-inducible gene I (RIG-I) signaling remains poorly understood. Herein, we show that cells on soft vs. rigid substrates elicit cytoskeletal prestress-dependent activation of RIG-I signaling, leading to differential type-I interferon (IFN) gene expression. Cells were cultured on soft (0.6 kPa) and stiff (8.5 kPa) substrates to modulate cellular traction and prestress, followed by transfection of Poly(I:C), a synthetic viral dsRNA mimic, to measure the RIG-I-mediated innate immune response. Cells on soft substrates show minimal activation of RIG-I signaling, resulting in low expression of IFN-β1 and other IFN-stimulated genes (ISGs), compared to cells on stiff substrates. We further demonstrate that activation of TANK Binding Kinase 1 (TBK1), a downstream effector of the RIG-I pathway, is inhibited in cells on soft substrates due to the cytoplasmic sequestration of the Yes-associated protein (YAP), a HIPPO pathway effector protein. In contrast, cells on stiffer substrates experienced decreased TBK1 inhibition due to the nuclear localization of YAP and exhibited elevated TBK1 activation and heightened IFN and ISG expressions. Together, we demonstrate that cytoskeletal prestress represents a key biophysical regulator of innate immune signaling. 

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. 

Autonomous vehicle trajectory tracking control is challenged by situations of varying road surface friction, especially in the scenario where there is a sudden decrease in friction in an area with high road curvature. If the situation is unknown to the control law, vehicles with high speed are more likely to lose tracking performance and/or stability, resulting in loss of control or the vehicle departing the lane unexpectedly. However, with connectivity either to other vehicles, infrastructure, or cloud services, vehicles may have access to upcoming roadway information, particularly the friction and curvature in the road path ahead. This paper introduces a model-based predictive trajectory-tracking control structure using the previewed knowledge of path curvature and road friction. In the structure, path following and vehicle stabilization are incorporated through a model predictive controller. Meanwhile, long-range vehicle speed planning and tracking control are integrated to ensure the vehicle can slow down appropriately before encountering hazardous road conditions. This approach has two major advantages. First, the prior knowledge of the desired path is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the controller to avoid violation of tire force limits while keeping performance and stability guarantees. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a sharply curving road with varying friction conditions, with results showing that the controller can drive a vehicle up to the handling limits and track the desired trajectory accurately. 

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.