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Ongoing Research

GPS-Free Terrain-Based Localization Using Vision Processing

GPS systems face severe hurdles for vehicle localization in the consumer market and military arenas. This can be verified by anyone who has tried to use on-vehicle GPS to measure their location within the urban canyons of big cities, or in the bottom lane of an elevated freeway, or in tight canyons, under forested canopies, or in poor weather. For millions of years, our ancestors kept track of their location and orientation without GPS, by looking at invariant features in far distance. The same method is used here as a means to estimate a vehicle's orientation in space. These estimates are important because several key chassis instabilities of a vehicle, namely sliding and rollover, require measurement or estimation of a vehicle's angular orientation in space.

Dr. S. Brennan and his research group at the Pennsylvania Transportation Institute are developing alternative vehicle measurement systems that use previously stored knowledge of the driving terrain. Like a blind person navigating through a complex world by constantly comparing a small set of cues to a recalled "map", Brennan's algorithms allow vehicles to similarly use stored knowledge of the driving environment to better interpret terrain-induced sensor cues and thereby extract vehicle position and orientation without GPS. Currently position estimates on the order of 10cm and orientation estimates within 0.2 degrees are being obtained in this research project via terrain-based cues alone. Ongoing work is integrating these measurements with existing GPS and INS position measurements.

Real-Time Terrain Mapping

Many safety-critical features near roadways are never surveyed after their construction, or are not resurveyed after geometric changes. These features include median geometries, locations of roadside or in-median barriers, shoulder drop-offs, rockfall faces, and offset distances to vegetation and other barrier-type geometries. Additionally, many roadways are poorly characterized geometrically including the vertical and horizontal curves, superelevation, and in some cases, even the location of the roadway itself!

This project is investigating the use of LIDAR laser-scanning techniques to extract roadway geometry features over very long segments of divided interstate highway (up to 6000 to 8000 miles across 5 to 6 states) with the intent to generate a database of major roadway features. This database will directly support an ongoing project on the influence of highway geometry on vehicle dynamics, and will also provide a valuable dataset for monitoring, visualization, and navigation in the future. Video

Investigators: Pramod Vemulapalli, Vishi Gupta, Dr. Sean Brennan, Dr. Eric Donnell
Sponsors: National Academies, NCHRP 22-21 pooled fund in partnership with the Midwest Research Institute

GPS-Free Terrain-Based Localization Using Vehicle Disturbances

Have you ever been annoyed by the repetitive vibration in your vehicle caused by uneven paving in the highway? And if so, did you change lanes to find a smoother ride? Well, this might perturb you, but your vehicle might find this to be a vital clue to where you are located and where you are headed. In fact, your future vehicle might use this to locate itself with the same accuracy as GPS.

GPS navigation is becoming very common in today's vehicles, however, poor reception, low satellite visibility, and the ease of jamming a GPS signal in battlefield operation has continued the interest in GPS-less navigation techniques. Also, GPS is not reliable enough for consumer vehicle automation. The Intelligent Vehicles and Systems research group has developed an algorithm that can localize a vehicle with sub-meter accuracy along the roadway simply by comparing a vehicle's disturbance response with a terrain map, similar to maps stored on common TomTom devices. Having a redundant way of estimating vehicle position is an important step in advancing the state of the art vehicle automation and driver assist technology.

Distributions of Vehicle Parameters and their associated Dimensionless Parameters

This study involves analyzing the distributions of vehicle parameters and their associated dimensionless parameters. The vehicle parameters used in the study are related to the bicycle model as well as roll and pitch dynamics. The parameters have been primarily collected and synthesized from the database of the National Highway Traffic Safety Administration (NHTSA) and more than 270 vehicle-dynamics-related literatures. A group of non-dimensional parameters has been created using dimensional analysis and the Buckingham Pi theorem. The reduced number of dimensionless parameters is being used to analyze system similarity across size scales.

Investigators: Sittikorn Lapapong, Dr. Sean Brennan
Sponsors: The Thomas D. Larson Pennsylvania Transportation Institute

Driver Assist Technologies for Platooning Heavy Vehicles - Convoy Collision Avoidance

Military convoys often operate in unpredictable and dangerous situations, including inclement weather conditions and combat zones subject to unexpected attack. This research initiative focuses on mitigating rear-end collisions by improving driver-assist systems and vehicle-to-vehicle communications. This study will examine commercial-off-the-shelf technologies and their compatibility with sensor/algorithm combinations to be installed on existing military vehicles.

The goal of the study is to produce an inexpensive, unobtrusive, and easily-incorporated solution to warn drivers of impending collisions or unsafe driving situations. This project will also examine other convoy-assist research including individual vehicle status monitoring within a convoy, GPS breadcrumb-based platooning, and vehicle string stabilization.

Investigators: Stephen Chaves, Sanket Amin, Dr. Joel Anstrom - LTI, Dr. S. Brennan - MNE/LTI, Ed Crow - Applied Research Lab, Karl Reichard - Applied Research Lab
Sponsors: Department of Defense, Army TACOM

FTA Reliability Correlation Between Test Track and In-Service Data (Phase 2) − Modeling Failure Rates in Complex Systems

This ongoing study follows a previous study which analyzed the correlation between reliability of in-transit buses and results of bus testing performed at the Thomas D. Larson Pennsylvania Transportation Institute (LTI). This study seeks to build on earlier results by analyzing failures at a subsystem level. It also seeks to quantify how interactions between subsystems and subsystem failures affect the overall failure rate of a bus. Further, the study will examine the effects of operational factors such as bus route, maintenance schedules, repair crew etc. on the failure rate.

The results of these investigations will hopefully improve knowledge about causality, predictability, and frequency of subsystem failures. This in turn may help transit agencies in keeping track of inventory requirements and scheduling maintenance. It may also greatly assist designers improve bus designs by quantifying interdependency between subsystems, and suggest new ways to validate the reliability of subsystem designs.

Investigators: Kshitij Jerath, Dr. Sean Brennan
Sponsors: The Federal Transit Administration

HyPER: Hybrid Power and Energy for Robotics

Investigators: Drew Logan, Dr. Sean Brennan
Sponsors: Department of Defense, NAVEOD Division

Interfacing Immersive Driving Simulator with CarSimDS Live Vehicle Dynamics Software

To study the interaction of the human driver with a vehicle, this project has begun rebuilding the existing driving simulator at the Thomas D. Larson Pennsylvania Transportation Institute with the live driving simulator software provided by CarSim. Current work incorporates the interfacing of three live animators from CarSim with three frontal projection screens. This will provide a 140 degree field of vision from the driver seat of the Mac truck cab. Future work will involve combining this system with EEG and eye-tracker measurements, as well as interfacing the driving simulator with live, external vehicles to allow for realistic study of human-driver interaction.

In-vehicle Driving Behavior Field Study

Naval Explosive Ordinance Disposal Product Family

The Naval Explosive Ordnance Disposal (NAV EOD) Product Family project encompasses research into the conceptual robot design methodology, underlying physics, and experimental validation. This project explores the allometry of mobile robotics, both on the component and system level. Predictive performance models for wheeled and tracked robots are being developed as part of this project to describe their designs capabilities (climbing, speed, maneuverability, etc.). Each of these models are compared with and validated against a variety of mobile EOD robots.

The developed models will allow quick generation of thousands of conceptual robot designs representing the feasible design space based upon existing technologies. This design space can then be visually explored using a Trade Space Visualization (TSV) tool (developed by the Penn State Applied Research Laboratory (Penn State-ARL)). These tools will facilitate informed decision-making early in the design process of new robots, or suggest modifications to existing robots. By aiding in correct decision-making early in the design phase, these tools may prevent project delays and cost overruns associated with major project changes made later in the design and development phases.

Investigators: Drew Logan, Dr. Sean Brennan, Dr. Tim Simpson, Dr. Karl Reichard, Dr. Chris Rogan
Sponsors: NAVEOD, Navy

Next Generation Vehicle Positioning Techniques in GPS-Degraded Environments for Vehicle Safety and Automation Systems

The next generation of safety and vehicle automation will rely on precise positioning, yet GPS-based positioning is hampered by frequent blockages of the GPS signal, even in normal driving situations. This project seeks to develop broader approaches to vehicle positioning that does not rely exclusively on GPS. Because there is no one “silver bullet” to replace or augment GPS, this proposed work seeks to achieve a major improvement in vehicle positioning performance by developing a multi-faceted approach that combines the strengths of alternative localization methods: terrain-based localization, visual odometry, and radio frequency (RF) ranging based on DSRC radio technology. Based on testing and characterization of these technologies individually in a test track environment, we are developing data fusion algorithms that collectively create an Integrated Positioning System (IPS) for degraded GPS environments. This work supports an ongoing FHWA EAR work at Auburn in fusion of GPS and on-board sensors. This integrated approach will blend the strengths of each technique to create an exceptionally precise and reliable positioning system.

PSU's Hardware-in-the-Loop Network

This project seeks to develop a first-of-its-kind campus-wide Hardware-in-the-Loop network at Penn State University. This network uses internet-enabled data-acquisition systems and local power-processing interfaces to connect vehicle subsystems - fuel cells, novel batteries, ultracapacitors, flywheel energy storage systems, high efficiency electric drive motors, advanced combustion engine test stands, and chassis dynamometers - in different laboratories distributed over the wide geographic area of the Penn State campus. These components can "talk" with each other just as they would interact in a vehicle, and thus enable the study of the coupled interaction of these components. Hence, researchers can better understand advanced Hybrid Electric Vehicle (HEV) architectures, components, simulation models, and energy management controllers without requiring construction of a new vehicle for each experiment and thus advance understanding of such architectures that are years or decades from integration.

The core challenge in developing a new HIL system is to solve issues of forming and operating a networked, embedded, distributed data-acquisition and control system where each node consists of a hardware or software representation of physical hardware. For most engineering systems, vehicles included, these nodes will often interact in a closed-loop manner such that unsupervised connectivity loss at each node may have catastrophic safety, cost, and data-quality implications. While other faculty are addressing the hardware platform deployments, our interest is to understand the influence of the node-to-node HIL communication interface including development and validation of low-order models of the hardware at each node. The HIL-specific challenges include finding communication sample rates and data lengths sufficient to maintain equivalence to true systems (Speed), finding scaling factors on the data such that one system can be correctly resized to interact with another (Scaling), and finding means to switch seamlessly between hardware and software representations such that there is no perceptible change in data (Switchover).

Pursuit Management: Attribute-based Evaluation of Stop stick, Spike strip and Other Tire Deflation Technologies

There are many high-speed pursuit situations where there are simply no technological means to stop a pursued vehicle, and there are also many situations known to be quite dangerous to the officers involved. The National Institute of Justice, in a long-term effort to improve the effectiveness and safety of police pursuits, wishes to evaluate the safety and effectiveness issues involved with terminating a pursuit using a tire deflation device to incapacitate a pursued vehicle. This study will involve testing the commonly used devices at Mid-State Airport at varying speeds, varying tire technology, and with/without concurrent vehicle maneuvers i.e. a swerve. The test vehicle will be a fully instrumented, fully automated (un-manned) LTI fleet vehicle configured to emulate a car that may typically be involved in a high-speed police pursuit.

Stability Analysis of Complex Behavior in Emergent Traffic Jams

With increasing levels of vehicle automation and driver assist entering the market, there is concern about the interaction of vehicle algorithms with each other and with the other human drivers on the road. This is particularly exacerbated by the slow reaction times of humans relative to computer algorithms, and that automated vehicle safety algorithms are in general designed to be reactive during the clear onset of accident situations rather than proactively preventing accidents. This project examines the stability of mixed automated/human vehicle drivers in a highway context using a complex systems approach. Complex systems are made up of a number of interacting components, which may be identical or distinct. Often, the interactions between these components result in a behavior that cannot be deduced from the individual components. A similar phenomenon is observed when interacting vehicles cause emergent or “phantom” traffic jams on highways with no apparent root cause. These jams result in a reduction of highway capacity and often persist for long periods of time, causing additional losses in terms of fuel consumption and emissions.

This ongoing study seeks to analyze the conditions under which dangerous emergent behavior occurs. It also seeks to set up a framework under which the stability of such systems may be analyzed. Further, the study will examine the effects that automated agents (vehicles with automatic cruise control or similar technologies) may have on the stability of traffic flows on highways, with the intent to validate the stability of various algorithms for driver assist in the context of a complex traffic flow. It may also provide a way to increase highway capacity without additional infrastructure.

Investigators: Kshitij Jerath, Dr. Sean Brennan

Distributed Power Management Project at the Philadelphia Navy Yard

Control systems are essential to improving the efficiency and reducing the cost of energy in buildings and vehicles. Power control systems today are being developed across a wide variety of scales and domains, each sharing similar problems and concerns. These include power management within hybrid vehicle powertrain systems to manage fuel economy and emissions, the management of power flows across regional power grids to coordinate geographically distributed power sources and users, the management of power production and usage to increase efficiency within local and regional grids and residential, commercial, and industrial buildings, to name just a few.

The goal of this project is to achieve the vision of distributed power production and management systems utilizing the synchronization of technologies for sustainable energy production, storage, distribution and use at various scales, with a primary focus on micro-grids. The Navy Yard, with its array of government, industry, and academic partners, existing energy research and development activity, and its combination of newly constructed buildings and existing structures undergoing adaptive reuse, is an ideal site for exploring these concepts. Hardware and control algorithms that support supply-aware load control are being developed. Specifically, power management control systems that coordinate power production of wind and solar systems with the heating and cooling power needs of structures are being explored. This includes energy demands from pluggable hybrids.
 

Investigators: Dr. Sean Brennan
Sponsors: Ben Franklin Technology Development Association

Previous Research (Click here)