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abstract: The prospects of commercially available autonomous vehicles are surely tantalizing, however the implementation of these vehicles and their strain on the social dynamics between motorists and pedestrians remains unknown. Questions concerning how autonomous vehicles will communicate safety and intent to pedestrians remain largely unanswered. This study examines the efficacy of various proposed technologies for bridging the communication gap between self-driving cars and pedestrians. Displays utilizing words like “safe” and “danger” seem to be effective in communicating with pedestrians and other road users. Future research should attempt to study different external notification interfaces in real-life settings to more accurately gauge pedestrian responses. / Dissertation/Thesis / Masters Thesis Engineering 2019

A comparison of viscous hydrodynamic force models is presented, with application on an autonomous underwater vehicle (AUV). The models considered here are \emph{quasi-steady}, meaning that force is expressed as a function of instantaneous vehicle state. This is in contrast to physical reality, where the force applied to a rigid body moving through a viscous fluid is history-dependent. As a result, the comparison of models is restricted to how well they are able to recreate a force history, rather than how closely they represent the underlying physics. Of the models under consideration, no single model performs significantly better than the others, but several perform worse. Each viscous hydrodynamic force model presented here is expressed as a linear combination of basis functions, which are nonlinear functions of body-relative velocity. The greater dynamical model is presented in a rigid-body framework with six degrees of freedom, with terms which account for inviscid fluid flow, restoring forces due to gravity, and control forces due to actuator motion. The models are selected from several that have been proposed in the literature, which include empirically-derived and physics-based models. Some models assume that the relationship between force and velocity is fundamentally linear or quadratic in nature, or make assumptions about coupled motion. The models are compared by their relative complexities, and also by their ability to reproduce data sets generated from field experiments. The complete dynamical equations are presented for each model, including coefficients suitable for use with the Virginia Tech 690 AUV. / Master of Science

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"Autonomous vehicle is an engineering technology that can improve transportation safety, alleviate traffic congestion and reduce carbon emissions. Research on autonomous vehicles can be categorized by functionality, for example, object detection or recognition, path planning, navigation, lane keeping, speed control and driver status monitoring. The research topics can also be categorized by the equipment or techniques used, for example, image processing, computer vision, machine learning, and localization. This dissertation primarily reports on computer vision and machine learning algorithms and their implementations for autonomous vehicles. The vision-based system can effectively detect and accurately recognize multiple objects on the road, such as traffic signs, traffic lights, and pedestrians. In addition, an autonomous lane keeping system has been proposed using end-to-end learning. In this dissertation, a road simulator is built using data collection and augmentation, which can be used for training and evaluating autonomous driving algorithms. The Graphic Processing Unit (GPU) based traffic sign detection and recognition system can detect and recognize 48 traffic signs. The implementation has three stages: pre-processing, feature extraction, and classification. A highly optimized and parallelized version of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is used. The system can process 27.9 frames per second with the active pixels of a 1,628 by 1,236 resolution, and with the minimal loss of accuracy. In an evaluation using the BelgiumTS dataset, the experimental results indicate that the detection rate is about 91.69% with false positives per window of 3.39e-5, and the recognition rate is about 93.77%. We report on two traffic light detection and recognition systems. The first system detects and recognizes red circular lights only, using image processing and SVM. Its performance is better than that of traditional detectors and it achieves the best performance with 96.97% precision and 99.43% recall. The second system is more complicated. It detects and classifies different types of traffic lights, including green and red lights in both circular and arrow forms. In addition, it employs image processing techniques, such as color extraction and blob detection to locate the candidates. Subsequently, a pre-trained PCA network is used as a multi-class classifier for obtaining frame-by-frame results. Furthermore, an online multi-object tracking technique is applied to overcome occasional misses and a forecasting method is used to filter out false positives. Several additional optimization techniques are employed to improve the detector performance and to handle the traffic light transitions. A multi-spectral data collection system is implemented for pedestrian detection, which includes a thermal camera and a pair of stereo color cameras. The three cameras are first aligned using trifocal tensor, and the aligned data are processed by using computer vision and machine learning techniques. Convolutional channel features (CCF) and the traditional HOG+SVM approach are evaluated over the data captured from the three cameras. Through the use of trifocal tensor and CCF, training becomes more efficient. The proposed system achieves only a 9% log-average miss rate on our dataset. Autonomous lane keeping system employs an end- to-end learning approach for obtaining the proper steering angle for maintaining a car in a lane. The convolutional neural network (CNN) model uses raw image frames as input, and it outputs the steering angles corresponding to the input frames. Unlike the traditional approach, which manually decomposes the problem into several parts, such as lane detection, path planning, and steering control, the model learns to extract useful features on its own and learns to steer from human behavior. More importantly, we find that having a simulator for data augmentation and evaluation is important. We then build the simulator using image projection, vehicle dynamics, and vehicle trajectory tracking. The test results reveal that the model trained with augmented data using the simulator has better performance and achieves about a 98% autonomous driving time on our dataset. Furthermore, a vehicle data collection system is developed for building our own datasets from recorded videos. These datasets are used in the above studies and have been released to the public for autonomous vehicle research. The experimental datasets are available at http://computing.wpi.edu/Dataset.html."

The development, implementation, and testing of a leader-follower based robust nonlinear formation controller is discussed in this thesis. This controller uses sliding mode control on the length and angle between the leader and follower vessels to produce the desired formation. A boat model, assuming planar motion (three degrees of freedom), is used as the bases for the controller. Open loop testing is performed to determine parameter values to match the simulation model to the physical one and, upon tuning of the controller to match, closed loop testing of the controller with a virtual leader is also performed. From these tests it is found that the controller is unstable, thus improvements to the controller, through changes made to the model and to the parameter identification process, are undertaken. Simulations comparing the initial and updated models of the vehicle to open loop data show an improvement in the new model.

It is known in the literature on Automated Highway Systems that information flow can significantly affect the propagation of errors in spacing in a collection of vehicles. This thesis investigates this issue further for a homogeneous collection of vehicles. Specifically, we consider the effect of information flow on the propagation of errors in spacing and velocity in a collection of vehicles trying to maintain a rigid formation. The motion of each vehicle is modeled using a Linear Time Invariant (LTI) system. We consider undirected and connected information flow graphs, and assume that that each vehicle can communicate with a maximum of q(n) vehicles, where q(n) may vary with the size n of the collection. The feedback controller of each vehicle takes into account the aggregate errors in position and velocity of the vehicles, with which it is in direct communication. The controller is chosen in such a way that the resulting closed loop system is a Type-2 system. This implies that the loop transfer function must have at least two poles at the origin. We then show that if the loop transfer function has three or more poles at the origin, and if the size of the formation is sufficiently large, then the motion of the collection is unstable. Suppose l is the number of poles of the transfer function relating the position of a vehicle with the control input at the origin of the complex plane, and if the number (q(n)l+1)/(nl) -> 0 as n -> (Infinity), then we show that there is a low frequency sinusoidal disturbance with unity maximum amplitude acting on each vehicle such that the maximum errors in spacing response increase at least as much as O (square_root(n^l/(q(n)^(l+1)) ) consequence of the results presented in this paper is that the maximum of the error in spacing and velocity of any vehicle can be made insensitive to the size of the collection only if there is at least one vehicle in the collection that communicates with at least O(square_root(n)) other vehicles in the collection.

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In a variety of military operations, a convoy of autonomous followers may need to traverse the leader's path without using Global Positioning System (GPS), lane markers/magnets and/or a vision-based vehicle-following system. This can be achieved by using Visual Teach and Repeat (VT and R), which provides an effective method for autonomous repeating of a previously driven path. This thesis describes the design of a distributed control system that uses the idea behind the VT and R method to allow a convoy of inter-communicable autonomous vehicles to follow a manually-driven lead vehicle's path with a desired inter-vehicle spacing, even when the leader is not in the camera view of the followers. The longitudinal controller is designed for addressing a 1D spacing problem and then combined with a path tracker for tracking a path in a 2D environment. The designed control model is tested in simulations.

In a variety of military operations, a convoy of autonomous followers may need to traverse the leader's path without using Global Positioning System (GPS), lane markers/magnets and/or a vision-based vehicle-following system. This can be achieved by using Visual Teach and Repeat (VT and R), which provides an effective method for autonomous repeating of a previously driven path. This thesis describes the design of a distributed control system that uses the idea behind the VT and R method to allow a convoy of inter-communicable autonomous vehicles to follow a manually-driven lead vehicle's path with a desired inter-vehicle spacing, even when the leader is not in the camera view of the followers. The longitudinal controller is designed for addressing a 1D spacing problem and then combined with a path tracker for tracking a path in a 2D environment. The designed control model is tested in simulations.