Several driving situations exist where fuel-optimal driving in terms of aggregate performance can only be achieved when one or more vehicles incurs a sacrifice in its own fuel consumption. For these situations, an economic incentive is needed to entice that vehicle to participate in aggregate fuel-optimal driving. Focusing on platooning amongst automated heavy-duty vehicles and using real trucking routes, we examine the precise extent to which the benefits of platooning can be expanded through the incorporation of exchange-based incentives. We focus on two mechanisms for incentivized platooning: (i) incentivized “catch-up” along a prescribed highway route and (ii) incentivized re-routing to allow for platooning. For the incentivized “catch-up” mechanism, platoon capable vehicles begin at staggered positions, using a novel platoon catch-up algorithm capable of determining the fuel-optimal platoon engagement position and fuel-optimal velocity trajectories. Additionally, the incentivized re-routing mechanism determines the optimal route for a network of platoon-capable vehicles, allowing for a vehicle to reroute its trajectory to engage within the platoon. Because such scenarios will be shown to frequently lead to aggregate benefit, while actually hurting the fuel economy of one or more participants, we propose three methods for explicitly computing the monetary value of the exchange. Assuming a known trajectory and traffic pattern, the first uses the Shapley value to determine the exchange value. The second method adjusts the Shapley value, accounting for uncertainty associated with traffic modeling. The final method assumes a competitive market, requiring each individual operator to implement a bid.

This paper presents an in-vehicle Human Machine Interface (HMI) that provides speed advisories to the driver leveraging Signal Phase and Timing (SPaT) information which can be obtained using Vehicle-to-Infrastructure (V2I) technologies. The goal of developing this HMI is to improve driving safety as well as vehicle fuel economy. To achieve this goal, we use a hierarchical predictive online controller for computation, a Robotic Operating System (ROS) framework for data transmission and an HTML/JavaScript based HMI for user-friendly information display. We experimentally validate the HMI on a real heavy-duty vehicle by replacing the conventional speedometer with the HMI. The results show i) the traffic signal bands enable the driver to change behavior based on anticipated red and green signal scenarios and ii) by using the speed advisories provided by the HMI, an 8.71% fuel economy improvement is achieved in experimental tests when using the HMI compared to baseline tests.

Visual odometry methods are increasingly being used to estimate a vehicle's ego-motion from range data due to the decreasing cost of range sensors and the impressive speed and accuracy of visual odometry techniques. Dense geometry-based visual odometry methods are fundamentally based on the range flow constraint equation, an equation which depends on the temporal and spatial derivatives of range images. However, these derivatives are calculated with the fundamental assumption that the range flow is magnitude-limited. When scaling this method for faster vehicles, this assumption could be violated, invaliding the range flow constraint equation and thus corrupting the resulting ego-motion estimates. This paper derives the sensor, motion, environment, and sampling frequency conditions that would mathematically violate the range flow constraint. This information is useful for defining the operational limits of dense geometry-based visual odometry methods.

The severity of a road departure event strongly depends on the features around the roadway: trees and other “hard” fixed objects represent a severe collision hazard, steep slopes nearby may be representing rollover threats or frontal impact hazards, and sharp road-edge drop-offs may exist that prevent high-speed road recovery. But the near-road area may also contain traversable shoulders and medians, such that a road departure can be a fully safe and recoverable event. This paper presents a simulated road departure warning system, sensitive to severity of hazards based on near-road terrain geometry analysis and subsequent threat assessment. To serve as a demonstration, many random 3D models of a highway road and near-road features following AASHTO guidelines were generated. Near-road features were subdivided into three categories corresponding to high, medium or low severity based on their geometries. We assume that geometric parameters of features used in this study are available from pre-processed LIDAR or other map data. Due to unavailability of threat correlation between different types of features, a relative threat index defined as Normalized Average Severity index is used to determine threats associated with a feature. To simulate a driver-warning system, geometries and hazards were tagged with different colors on the generated 3D model. The 3D model is designed to serve as an additional visual warning system for the driver providing information about risk zones nearby the present vehicle position.

This paper proposes a model-based method to estimate lateral planar vehicle states using a forward-looking monocular camera, a yaw rate gyroscope, and an a priori map of road superelevation and temporally previewed lane geometry. Theoretical estimator performance from a steady-state Kalman-filter implementation of the estimation framework is calculated for various look-ahead distances and vehicle speeds. The application of this filter structure to real driving data is also explored, along with error characteristics of the filter on straight and curved roads, with both superelevated and flat profiles. The effect of superelevation on estimator performance is found to be significant. Experimental and theoretical analysis both show that the benefits of state estimation using previewed lane geometry improve with increasing lane preview, but this improvement diminishes due to increased lane tracking errors at distances beyond 20 m ahead of the vehicle.

The advent of intelligent vehicle technologies holds significant potential to alter the dynamics of traffic flow. Prior work on the effects of such technologies on the formation of self-organized traffic jams has led to analytical solutions and numerical simulations at the mesoscopic scale, which may not yield significant information about the distribution of vehicle cluster size. Since the absence of large clusters could be offset by the presence of several smaller clusters, the distribution of cluster sizes can be as important as the presence or absence of clusters. To obtain a prediction of vehicle cluster distribution, the included work presents a statistical mechanics-inspired method of simulating traffic flow at a microscopic scale via the generalized Ising model. The results of the microscopic simulations indicate that traffic systems dominated by adaptive cruise control ( acc)-enabled vehicles exhibit a higher probability of formation of moderately sized clusters, as compared with the traffic systems dominated by human-driven vehicles; however, the trend is reversed for the formation of large-sized clusters. These qualitative results hold significance for algorithm design and traffic control because it is easier to predict and take countermeasures for fewer large localized clusters as opposed to several smaller clusters spread across different locations on a highway.

This paper describes a dynamical model-based method for the localization of road vehicles using terrain data from the vehicle's onboard sensors. Road data are encoded using linear dynamical models and then, during travel, the location is identified through continuous comparison of a bank of linear models. The approach presented has several advantages over previous methods described in the literature. First, it creates computationally efficient linear model map representations of the road data. Second, the use of linear models eliminates the need for metrics during the localization process. Third, the localization algorithm is a computationally efficient approach that can have a bounded localization distance in the absence of noise, given certain uniqueness assumptions on the data. Fourth, encoding road data using linear models has the potential to compress the data, while retaining the sensory information. Finally, performing only linear operations on observed noisy data simplifies the creation of noise mitigation algorithms.

While decades of work and hundreds of research papers exist on unicycle robot control, the control of skid-steer robots is not yet as standardized due to the complexity of wheel slipping behavior. This work presents a method of utilizing the track or wheel Instantaneous Centers of Rotation (ICRs) on a skid-steer vehicle to map skid-steer dynamics to an equivalent time-varying model of unicycle dynamics. This allows for the direct implementation of existing unicycle, or Hilare type, robot trajectory controllers. Knowledge of ICR locations enables the calculation of required track or wheel speeds to create desired vehicle movement, similar to the kinematic relations resulting from the no-slip assumption of a unicycle robot. The algorithm requires no prior knowledge of vehicle dimensions or terrain parameters because ICR locations are estimated during robot operation using an extended Kalman filter (EKF). Simulation and experimental results for a wheeled skid-steer vehicle show good trajectory tracking performance.

Computer-based guidance of passenger vehicles is a common reality today, but cost, computation, and robustness challenges remain to obtain accurate vehicle state estimates. This study builds on previous work by the authors towards the development of a vehicle state estimation framework that uses optimal preview control theory to fuse map, GPS, inertial, and forward-looking camera information in a linear filter that offers a-priori predictions of state estimate accuracy. By designing an optimal preview controller around a preview filter designed to make full use of a test vehicle's low-cost sensors, on-board map, and available visibility, a matched perception and control system is obtained. The resulting preview-based guidance system has a structure similar to LQG algorithms, and is tested both in simulation and on a real vehicle. The closed loop system provides lane-level tracking performance with low cost sensors.

This paper proposes a method for using a forward-looking monocular camera along with previewed road geometry from a high-fidelity, low-dimensional map to estimate lateral planar vehicle states by measuring the vehicle's temporally anticipated reference trajectory. Theoretical estimator performance from a steady-state Kalman Filter implementation of the estimation framework is calculated for various look-ahead distances and vehicle speeds. Application of this filter structure to real driving data is also briefly discussed. The use of temporally previewed measurements of a vehicle's reference path is shown to greatly improve the accuracy of vehicle planar state estimates, and shows promise for use in closed-loop lane keeping and driver assist applications.

This paper describes the development of a Hardware-in-the-Loop (HIL) driving simulator designed with ROS and Gazebo. An analysis of current driving simulator software is conducted focusing on the requirements for HIL simulators versus traditional simulator implementations. Finally, the process for implementing an open-source HIL-centric driving simulator is documented, along with an analysis of system performance emphasizing metrics of system latency and strategies to minimize inter-hardware delays.

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. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

Self-organizing traffic jams are known to occur in medium-to-high density traffic flows, and it is suspected that adaptive cruise control (ACC) may affect their onset in mixed human-ACC traffic. Unfortunately, closed-form solutions that predict the occurrence of these jams in mixed human-ACC traffic do not exist. In this paper, both human and ACC driving behaviors are modeled using the General Motors fourth car-following model and are distinguished by using different model parameter values. A closed-form solution that explains the impact of ACC on congestion due to the formation of self-organized traffic jams (or “phantom” jams) is presented. The solution approach utilizes the master equation for modeling the self-organizing behavior of traffic flow at a mesoscopic scale and the General Motors fourth car-following model for describing the driver behavior at the microscopic scale. It is found that, although the introduction of ACC-enabled vehicles into the traffic stream may produce higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.

In this study, we examine, for a six-link snake robot, how an optimal gait might change as a function of the snake- surface interaction model and how the overall locomotion performance changes under nonoptimal conditions such as joint failure. Simulations are evaluated for three different types of friction models, and it is shown that the gait parameters for serpentine motion are very dependant on the frictional model if minimum power expenditure is desired for a given velocity. Experimental investigations then motivate a surface interaction model not commonly used in snake locomotion studies. Using this new model, simulation results are compared to experiments for nominal and nonnominal locomotion cases including actuator faults. It is shown that this model quite accurately predicts locomotion velocities and link profiles, but that the accuracy of these predictions degrades severely at speeds where actuator dynamics become significant.

This paper investigates the behavior of a group of autonomous robots evolving in a polygonal environment according to a "move away from the closest neighbor" heuristic. We demonstrate that this distributed coordination algorithm optimizes an aggregate cost function that measures how uniformly distributed are the robots in their environment. Our technical approach based on non-smooth analysis and computational geometry unveils a sphere-packing problem. The algorithm is implemented in a testbed of indoor mobile robots equipped with sonar. We develop novel approaches for improving single point sonar scan performance. These algorithms are then shown to have improved reliability, resolution and speed in distributed environments as compared to other scanning methods.