Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles.

We present a novel perspective on how connected vehicles can reduce total vehicular delay arising due to the capacity drop phenomenon observed at fixed freeway bottlenecks. We analytically determine spatial regions upstream of the bottleneck, called zones of influence, where a pair of connected vehicles can use an event-triggered control policy to positively influence a measurable traffic macrostate, e.g.

At what cost? How planned collisions with pedestrians may save lives.

Pedestrian avoidance algorithms often tacitly assume that the maneuver which minimizes collisions will also be the safest maneuver. This work shows that this is not always the case when considering pedestrian fatalities. Given the unavoidable uncertainty in vehicle motion, environmental parameters, and pedestrian behavior, emergency avoidance maneuvers often involve some chance of a collision.

Effects Of Symmetry On The Structural Controllability Of Neural Networks: A Perspective.

The controllability of a dynamical system or network describes whether a given set of control inputs can completely exert influence in order to drive the system towards a desired state. Structural controllability develops the canonical coupling structures in a network that lead to un-controllability, but does not account for the effects of explicit symmetries contained in a network. Recent work has made use of this framework to determine the minimum number and location of the optimal actuators necessary to completely control complex networks.

Observability and Controllability of Nonlinear Networks: The Role of Symmetry.

Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. For example, noncontrollable mathematical models of real systems have subspaces that influence model behavior, but cannot be controlled by an input. Such subspaces can be difficult to determine in complex nonlinear networks.

Robust extrema features for time-series data analysis.

The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits.

Observability of Neuronal Network Motifs.

We quantify observability in small (3 node) neuronal networks as a function of 1) the connection topology and symmetry, 2) the measured nodes, and 3) the nodal dynamics (linear and nonlinear). We find that typical observability metrics for 3 neuron motifs range over several orders of magnitude, depending upon topology, and for motifs containing symmetry the network observability decreases when observing from particularly confounded nodes. Nonlinearities in the nodal equations generally decrease the average network observability and full network information becomes available only in limited regions of the system phase space.

Analyzing the influence of median cross-section design on highway safety using vehicle dynamics simulations.

Although vehicle dynamics simulations have long been used in vehicle design and crash reconstruction, their use for highway design is rare. This paper investigates the safety of highway medians through iterative simulations of off-road median encroachments. The commercially available software CarSim was used to simulate over one hundred thousand encroachments, representing the entire passenger vehicle fleet and a wide range of encroachment angles, departure speeds, steering inputs, and braking inputs.