Alexander A. Brown

Zero-moment point determination of worst-case manoeuvres leading to vehicle wheel lift

This paper proposes a method to evaluate vehicle rollover propensity based on a frequency-domain representation of the zero-moment point (ZMP). Unlike other rollover metrics such as the static stability factor, which is based on the steady-state behaviour, and the load transfer ratio, which requires the calculation of tyre forces, the ZMP is based on a simplified kinematic model of the vehicle and the analysis of the contact point of the vehicle relative to the edge of the support polygon. Previous work has validated the use of the ZMP experimentally in its ability to predict wheel lift in the time domain. This work explores the use of the ZMP in the frequency domain to allow a chassis designer to understand how operating conditions and vehicle parameters affect rollover propensity. The ZMP analysis is then extended to calculate worst-case sinusoidal manoeuvres that lead to untripped wheel lift, and the analysis is tested across several vehicle configurations and compared with that of the standard Toyota J manoeuvre.

Extending driving simulator capabilities toward Hardware-in-the-Loop testbeds and remote vehicle interfaces

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.

Superelevation Design for Sharp Horizontal Curves on Steep Grades

The objective of this study was to develop superelevation criteria for sharp horizontal curves on steep grades. Field studies were undertaken and vehicle dynamics simulations (point mass, bicycle, and multibody) were performed to investigate combinations of horizontal curve and vertical grade design criteria. The vehicle dynamics simulations used AASHTO design criteria and field-measured data to investigate the safety margins against skidding and rollover for several vehicle types on sharp horizontal curves with steep grades. Research results indicated that for a simple horizontal curve, the maximum rate of super elevation should not exceed 12% on a downgrade. A spiral curve transition is recommended if the maximum superelevation rate is greater than 12%. On upgrades of 4% and greater, the maximum superelevation rate should be limited to 9% for minimum-radius curves under certain conditions. The superelevation attained at the point of curve entry should be checked and compared with a lateral friction margin condition so that the lateral friction margin on curve entry is not less than the margin within the curve. On multilane highways, the “Stay in Lane” sign should be installed in advance of sharp horizontal curves on steep downgrades.

Superelevation Criteria for Sharp Horizontal Curves on Steep Grades

Sharp, horizontal curves on steep downgrades represent a potential safety concern for vehicles, especially heavy vehicles. Examples where this combination may occur are interchange ramp movements, curves on mountainous roads, or high-speed downgrade curves on controlled-access roadways. At these locations, the complicating factors of grade, pavement cross slope, and pavement friction fully tax the driver’s ability to provide correct vehicle positioning without compromising control of the vehicle. Superelevation criteria, horizontal curvature, and other associated geometric criteria needed to be developed for situations where steep grades are located on sharp horizontal curves. This report provides superelevation criteria for horizontal curves on steep grades. A series of field studies and vehicle dynamics simulations were undertaken to investigate combinations of horizontal curve and vertical grade design. Three classes of passenger vehicles and three classes of trucks were considered for safety analysis. The report provides design guidance based on the analyses for sharp horizontal curves on steep grades.

Lateral Vehicle State and Environment Estimation Using Temporally Previewed Mapped Lane Features

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.

Determination of Minimum State Preview Time to Prevent Vehicle Rollover

This research focuses on determining the minimum preview time needed to predict and prevent vehicle rollover. Statistics show that although rollover only occurs in 2.2% of total highway crashes, it accounts for 10.7% of total fatalities. There are several dynamic rollover metrics in use that measure a vehicle’s rollover propensity under specified conditions. However, in order to prevent a rollover event from occurring, it is necessary to predict a vehicle’s future rollover propensity. This research uses a novel vehicle rollover metric, called the zero-moment point (ZMP), to predict a vehicle’s rollover propensity. Comparing different amounts of preview, the results show that short-range predictions — as little as 0.75 seconds ahead of the vehicle — are sufficient to prevent nearly all dynamics-induced rollovers in typical shoulders and medians.Copyright

The promise of cyborg intelligence

SummaryYu et al. (2016) demonstrated that algorithms designed to find efficient routes in standard mazes can be integrated with the natural processes controlling rat navigation and spatial choices, and they pointed out the promise of such “cyborg intelligence” for biorobotic applications. Here, we briefly describe Yu et al.’s work, explore its relevance to the study of comparative cognition, and indicate how work involving cyborg intelligence would benefit from interdisciplinary collaboration between behavioral scientists and engineers.

Temporal preview estimation for design of a low cost lane-following system using a forward-facing monocular camera

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 vehicles 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.

Open-loop vehicle collision avoidance and rollover prevention using previewed Zero-Moment Point

This research estimates the minimum intervention distance needed for a vehicle to safely avoid an obstacle, while also preventing wheel lift and tire skid. The intervention strategy considered is an open-loop lane change avoidance trajectory. As the vehicle approaches the obstacle, it continually determines the available lane change maneuver, relying on predictions of the Zero-Moment Point (ZMP) metric to determine if lane change candidates may cause the onset of wheel lift. The approach used here considers both flat roads and banked curves that may occur due to highway design policy. Results show that larger distances between the vehicle and obstacle are needed to safely intervene when wheel lift and tire skid are considered.

Model-Based Vehicle State Estimation Using Previewed Road Geometry and Noisy Sensors

This paper proposes a method for using previewed road geometry from a high-fidelity map to improve estimates of planar vehicle states in the presence of unmodeled sensor bias errors. Using well-established, linear models for representing human driver behavior and for planar vehicle states, a causal link between previewed road geometry and vehicle states can be derived. Cast as an augmented, closed-loop linear system, the total driver-vehicle-road system’s states are estimated using a Kalman filter. Estimation results from this filter using simulated noisy measurements of vehicle states and map-based measurements of previewed road geometry are compared to standard Kalman filters with identical measurements of vehicle states alone. The effects of errors in driver modeling, vehicle nonlinearity, and measurement disturbances on the estimator’s fidelity are also examined and discussed.Copyright