Global Navigation Satellite Systems (GNSS) like the Global Positioning System (GPS) are susceptible to electronic interference which threatens the reliability of the systems outputs, precise time and localization. Interference comes from natural and predatory sources in the form of increased in-band noise and structured attacks. The structured attack, called spoofing, is designed to trick the receiver into reporting an incorrect navigation solution as if it were accurate. Modern automobiles are becoming more reliant on GPS for localization, automation, and safety. Vehicles are also equipped with a variety of sensors (e.g. Radars, Lidars, wheel encoders) that provide situational awareness which may be leveraged in a GPS spoofing detection scheme. The proposed spoofing detection and mitigation system relies on an existing Cooperative Adaptive Cruise Control (CACC) system to provide inter-vehicle ranging and data sharing. The inter-vehicle ranges are used to detect a spoofing attack, and the mitigation system removes the attacking signal from the incoming data stream. The spoofing detection and removal system is tested using data recorded with a fielded CACC system on two commercial trucks. Intermediate frequency (IF) GPS data is collected during the test. Since live sky spoofing is legal, the IF data recording allows for post process spoofing injection in a controlled environment. In post process, the spoofing signal is shown to “capture” the onboard GPS receiver. The proposed system uses the spoofed IF GPS data along with recorded observables from the CACC system to detection and remove the attack.

With the proliferation of mobile devices such as smartphones, an interesting problem is how to make use them to improve the accuracy of localization in indoor environments. In this paper, we develop a novel cooperative localization scheme exploiting mobility in the indoor environment. The problem is formulated as a semidefinite program (SDP) using Linear Matrix Inequality (LMI). With the proposed approach, mobile users utilize their top RSS measurements for distance estimation and to mitigate the the shadowing effect found in indoor environments. In addition, we utilize the estimated position for a user from the last time slot as a virtual access point (AP) to obtain the next position estimation, by utilizing the inertial measurement unit (IMU) data from smartphones. To better take advantage of the moving direction and velocity information provided by the smartphones, we next apply Kalman filter to further mitigate the errors in estimated positions. Simulation results confirm that both the mean error and variance can be effectively reduced by exploiting IMU data and Kalman filter.

This paper describes results to date of a project to prototype, evaluate, and test Driver-Assistive Truck Platooning (DATP), which could have significant positive safety and fuel savings potential for heavy truck operations. The project is led by Auburn University and funded within the Federal Highway Administration Exploratory Advanced Research program. This paper provides selected results from Phase One, which is currently exploring a range of technical and non-technical issues, including assessing real-world business and operational issues within the trucking industry. Specific technical sections address sensing and computing hardware; driver interface; sensor and actuator software and interfacing; control software; and operational environment.

A navigation filter combines measurements from sensors currently available on vehicles - Global Positioning System (GPS), inertial measurement unit, inertial measurement unit (IMU), camera, and light detection and ranging (lidar) - for achieving lane-level positioning in environments where stand-alone GPS can suffer or fail. Measurements from the camera and lidar are used in two lane-detection systems, and the calculated lateral distance (to the lane markings) estimates of both lane-detection systems are compared with centimeter-level truth to show decimeter-level accuracy. The navigation filter uses the lateral distance measurements from the lidar- and camera-based systems with a known waypoint-based map to provide global measurements for use in a GPS/Inertial Navigation System (INS) system. Experimental results show that the inclusion of lateral distance measurements and a height constraint from the map creates a fully observable system even with only two satellite observations and, as such, greatly enhances the robustness of the integrated system over GPS/INS alone. Various scenarios are presented, which affect the navigation filter, including satellite geometry, number of satellites, and loss of lateral distance measurements from the camera and lidar systems.

Complex high speed autonomous articulations associated with modern large-scale high degree-of-freedom (DOF) robotic arms have a high possibility of collision when integrated into human cooperative environments for human-aid, task automation, and biomedical interfacing. This paper proposes a large area collision detection system utilizing the piezoelectric effect of polyvinylidene fluoride film. The proposed system provides high dynamic range for sensation and robust adaptability to achieve collision detection on complex surfaces in order to augment robotic systems with collision perception. The design allows for increased cohabitation of human and high DOF robotic arms in cooperative environments requiring advanced and robust collision detection systems capable of retrofitting onto deployed and operating robotic arms in the commercial world.

In this paper, the authors develop an observer and controller for a robot and trailer system in which only a single non-collocated position measurement is used. A linearized state-space model of the system is derived using kinematic equations that have previously proven sufficient for state feedback control. Optimal observer gains (LQG) and controller gains (LQR) are calculated using known process and measurement noise variances. Simulations and experiments both confirm that the LQG/LQR system controls the nonlinear system in path tracking. Control based on state estimation yields performance that is comparable to a more heavily-instrumented full-state feedback system previously used on the robot-trailer system.

GPS receiver performance can suffer in difficult environments such as urban canyons and heavy foliage. Inertial sensors provide information between GPS updates and can enhance the position solution in a GPS/INS architecture. Additional information from safety sensors already on the vehicle, such as lane departure warning (LDW) sensors, can enhance the navigation solution further by constraining inertial errors even in the presence of GPS errors. This paper outlines a scalable navigation solution that can use a combination of GPS, reduced inertial sensors, full inertial data, vehicle CAN data, and vision sensors, depending on what data is available in difficult environments. Data was collected in Detroit, Michigan in a diverse mix of environments that includes heavy foliage, highway, and downtown areas, in proportions representative of what is expected in typical driving. Validation of the approach consists of both a qualitative analysis of the resulting trajectories overlaid on a map of the area and quantitative comparison of the trajectories produced by the proposed system and the reference system.

This paper demonstrates an evolutionary algorithm for the optimization of an autonomous control algorithm for a trained canine. Autonomous guidance is relevant because use of canines, though beneficial in many applications, is limited by the necessity of close human supervision. A rules-based expert system using GPS data was initially developed for this purpose. This rules-based system is not without limitations. Primarily, it takes a significant investment of trainer and developer time to derive appropriate values to use for control of the canine. A multi-objective fitness metric was developed to optimize for important parts of the control algorithm, and parameters of the algorithm were optimized using evolutionary computation. In simulations the evolved parameters fit the data better than the hand-tuned parameters, and preliminary field trials showed a 67% mission success rate, which shows the feasibility of evolving parameters for the control algorithm.

This paper analyzes a promising method of road grade estimation for its potential use in aiding ground vehicle electronic stability control systems. The method in question incorporates GPS and motion sensors into a basic Kalman Filter model to achieve clean, high update estimates of the vertical and longitudinal velocity states from which the grade can be easily calculated. The knowledge of road grade is incorporated into a sideslip estimation scheme, replacing the pitch rate sensor, and the improvement in the sideslip estimate is evaluated. Simulated results are shown, as are results from using experimental data comparing this sideslip estimate with one obtained which utilizes the pitch rate gyro.

The responsibility of a vision-based lane departure warning (LDW) system is to alert a driver of an unintended lane departure. Because these systems solely rely on the vision sensor's ability to detect the lane markings on the roadway, these systems are extremely sensitive to the roadway conditions. When a vehicle's LDW system fails to detect lane markers on the roadway, it loses its ability to alert the driver of an unintended lane departure. The goal of this research is to use GPS combined with inertial sensors and a high-accuracy map to assist a vision-based LDW system. GPS navigation systems are available in many automobiles, along with automotive-grade inertial sensors. The low accuracy of a typical GPS receiver found in an automotive navigation system is largely attributed to a position error. This error is too large to allow the GPS receiver to locate a vehicle in a particular lane on a roadway. A method to measure this error using a vision-based LDW system, together with a high-accuracy map, is presented in this paper. With the error known, the accuracy of the GPS receiver is increased to a high-enough level to localize the vehicle on a particular lane. Next, a method fusing GPS/inertial navigation sensor/vision and a high-accuracy map for highway lane tracking is presented. This method provides a backup lateral offset measurement that can be used for LDW when the LDW vision system loses track of the lane markings.

This paper details a unique method for estimating key vehicle states-body sideslip angle, tire sideslip angle, and vehicle attitude-using Global Positioning System (GPS) measurements in conjunction with other sensors. A method is presented for integrating Inertial Navigation System sensors with GPS measurements to provide higher update rate estimates of the vehicle states. The influence of road side-slope and vehicle roll on estimating vehicle sideslip is investigated. A method using one GPS antenna that estimates accelerometer errors occurring from vehicle roll and sensor drift is first developed. A second method is then presented utilizing a two-antenna GPS system to provide direct measurements of vehicle roll and heading, resulting in improved sideslip estimation. Additionally, it is shown that the tire sideslip estimates can be used to estimate the tire cornering stiffnesses. The experimental results for the GPS-based sideslip angle measurement and cornering stiffness estimates compare favorably to theoretical predictions, suggesting that this technique has merit for future implementation in vehicle safety systems

This paper presents a novel computer-controlled bicycle ergometer, the TiltCycle, for use in human biomechanics studies of pedaling. The TiltCycle has a tilting (reclining) seat and backboard, a split crank to isolate the left and right loads to the feet of the cyclist, and two belt-driven, computer-controller motors to provide both assistance and resistance loads. Sensors measure the kinematics and force production of the pedaling work performed, as well as goniometer and electromyography signals from the lower limbs. The technical description includes the mechanical design, low-level software and control algorithms designed for studies in human lower-limb biomechanics and bilateral coordination, and concludes with validation testing and system identification results.

This paper presents the application of an action module planning method to an experimental climbing robot named LIBRA. The method searches for a sequence of physically realizable actions, called action modules, to produce a plan for a given task. The search is performed with a hierarchical selection process that uses task and configuration filters to reduce the action module inventory to a reasonable search space. Then, a genetic algorithm search finds a sequence of actions that allows the robot to complete the task without violating any physical constraints. The results for the LIBRA climbing robot show the method is able to produce effective plans.