autonomous phd thesis

System modeling for connected and autonomous vehicles

Connected and autonomous vehicle (CAV) technologies provide disruptive and transformational opportunities for innovations toward intelligent transportation systems. Compared with human driven vehicles (HDVs), the CAVs can reduce reaction time and human errors, increase traffic mobility and will be more knowledgeable due to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. CAVs’ potential to reduce traffic accidents, improve vehicular mobility and promote eco-driving is immense. However, the new characteristics and capabilities of CAVs will significantly transform the future of transportation, including the dissemination of traffic information, traffic flow dynamics and network equilibrium flow. This dissertation seeks to realize and enhance the application of CAVs by specifically advancing the research in three connected topics: (1) modeling and controlling information flow propagation within a V2V communication environment, (2) designing a real-time deployable cooperative control mechanism for CAV platoons, and (3) modeling network equilibrium flow with a mix of CAVs and HDVs.

Vehicular traffic congestion in a V2V communication environment can lead to congestion effects for information flow propagation due to full occupation of the communication channel. Such congestion effects can impact not only whether a specific information packet of interest is able to reach a desired location, but also the timeliness needed to influence traffic system performance. This dissertation begins with exploring spatiotemporal information flow propagation under information congestion effects, by introducing a two-layer macroscopic model and an information packet relay control strategy. The upper layer models the information dissemination in the information flow regime, and the lower layer model captures the impacts of traffic flow dynamics on information propagation. Analytical and numerical solutions of the information flow propagation wave (IFPW) speed are provided, and the density of informed vehicles is derived under different traffic conditions. Hence, the proposed model can be leveraged to develop a new generation of information dissemination strategies focused on enabling specific V2V information to reach specific locations at specific points in time.

In a V2V-based system, multiclass information (e.g., safety information, routing information, work zone information) needs to be disseminated simultaneously. The application needs of different classes of information related to vehicular reception ratio, the time delay and spatial coverage (i.e., distance it can be propagated) are different. To meet the application needs of multiclass information under different traffic and communication environments, a queuing strategy is proposed for each equipped vehicle to disseminate the received information. It enables control of multiclass information flow propagation through two parameters: 1) the number of communication servers and 2) the communication service rate. A two-layer model is derived to characterize the IFPW under the designed queuing strategy. Analytical and numerical solutions are derived to investigate the effects of the two control parameters on information propagation performance in different information classes.

Third, this dissertation also develops a real-time implementable cooperative control mechanism for CAV platoons. Recently, model predictive control (MPC)-based platooning strategies have been developed for CAVs to enhance traffic performance by enabling cooperation among vehicles in the platoon. However, they are not deployable in practice as they require anembedded optimal control problem to be solved instantaneously, with platoon size and prediction horizon duration compounding the intractability. Ignoring the computational requirements leads to control delays that can deteriorate platoon performance and cause collisions between vehicles. To address this critical gap, this dissertation first proposes an idealized MPC-based cooperative control strategy for CAV platooning based on the strong assumption that the problem can be solved instantaneously. It then develops a deployable model predictive control with first-order approximation (DMPC-FOA) that can accurately estimate the optimal control decisions of the idealized MPC strategy without entailing control delay. Application of the DMPC-FOA approach for a CAV platoon using real-world leading vehicle trajectory data shows that it can dampen the traffic oscillation effectively, and can lead to smooth deceleration and acceleration behavior of all following vehicles.

Finally, this dissertation also develops a multiclass traffic assignment model for mixed traffic flow of CAVs and HDVs. Due to the advantages of CAVs over HDVs, such as reduced value of time, enhanced quality of travel experience, and seamless situational awareness and connectivity, CAV users can differ in their route choice behavior compared to HDV users, leading to mixed traffic flows that can significantly deviate from the single-class HDV traffic pattern. However, due to a lack of quantitative models, there is limited knowledge on the evolution of mixed traffic flows in a traffic network. To partly bridge this gap, this dissertation proposes a multiclass traffic assignment model. The multiclass model captures the effect of knowledge level of traffic conditions on route choice of both CAVs and HDVs. In addition, it captures the characteristics of mixed traffic flow such as the difference in value of time between HDVs and CAVs and the asymmetry in their driving interactions, thereby enhancing behavioral realism in the modeling. New solution algorithms will be developed to solve the multiclass traffic assignment model. The study results can assist transportation decision-makers to design effective planning and operational strategies to leverage the advantages of CAVs and manage traffic congestion under mixed traffic flows.

This dissertation deepens our understanding of the characteristics and phenomena in domains of traffic information dissemination, traffic flow dynamics and network equilibrium flow in the age of connected and autonomous transportation. The findings of this dissertation can assist transportation managers in designing effective traffic operation and planning strategies to fully exploit the potential of CAVs to improve system performance related to traffic safety, mobility and energy consumption.

Collaborative Research: Coordinated Real-Time Traffic Management Based on Dynamic Information Propagation and Aggregation under Connected Vehicle Systems

Directorate for Engineering

Degree Type

• Doctor of Philosophy
• Civil Engineering

Campus location

• West Lafayette

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•     Bib Dam, T. (2023). Sample Efficient Monte Carlo Tree Search for Robotics , Ph.D. Thesis .
•       Bib Flynn, H. (2023). PAC-Bayesian Bandit Algorithms With Guarantees , Ph.D. Thesis .
•       Bib Klink, P. (2023). Reinforcement Learning Curricula as Interpolations between Task Distributions , Ph.D. Thesis .
•       Bib Look, A. (2023). Deterministic Approximations for Deep State-Space Models , Ph.D. Thesis .
•   Bib Prasad, V.; (2023). Learning Human-Robot Interaction: A Case Study on Human-Robot Handshaking , Ph.D. Thesis .
•   Bib Urain, J. (2023). Deep Generative Models for Motion Planning and Control , Ph.D. Thesis .
•       Bib Abdulsamad, H. (2022). Statistical Machine Learning for Modeling and Control of Stochastic Structured Systems , Ph.D. Thesis .
•     Bib Becker-Ehmck, P. (2022). Latent State-Space Models for Control , Ph.D. Thesis .
•       Bib Belousov, B. (2022). On Optimal Behavior Under Uncertainty in Humans and Robots , Ph.D. Thesis .
•     Bib Cowen-Rivers, A. (2022). Pushing The Limits of Sample-Efficient Optimisation , Ph.D. Thesis .
•       Bib Arenz, O. (2021). Sample-Efficient I-Projections for Robot Learning , Ph.D. Thesis .
•     Bib Loeckel, S. (2021). Machine Learning for Modeling and Analyzing of Race Car Drivers , Ph.D. Thesis .
•     Bib Lutter, M. (2021). Inductive Biases for Machine Learning in Robotics and Control , Ph.D. Thesis .
•     Bib Muratore, F. (2021). Randomizing Physics Simulations for Robot Learning , Ph.D. Thesis .
•     Bib Tosatto, S. (2021). Off-Policy Reinforcement Learning for Robotics , PhD Thesis .
•     Bib Koert, D. (2020). Interactive Machine Learning for Assistive Robots , Ph.D. Thesis .
•     Bib Lampariello, R. (2020). Optimal Motion Planning for Object Interception and Grasping , Ph.D. Thesis .
•     Bib Tanneberg, D. (2020). Understand-Compute-Adapt: Neural Networks for Intelligent Agents , Ph.D. Thesis .
•     Bib Buechler, D. (2019). Robot Learning for Muscular Systems , Ph.D. Thesis .
•     Bib Ewerton, M. (2019). Bidirectional Human-Robot Learning: Imitation and Skill Improvement , PhD Thesis .
•     Bib Gebhardt, G.H.W. (2019). Using Mean Embeddings for State Estimation and Reinforcement Learning , PhD Thesis .
•     Bib Gomez-Gonzalez, S. (2019). Real Time Probabilistic Models for Robot Trajectories , Ph.D. Thesis .
•     Bib Parisi, S. (2019). Reinforcement Learning with Sparse and Multiple Rewards , PhD Thesis .
•     Bib Koc, O. (2018). Optimal Trajectory Generation and Learning Control for Robot Table Tennis , PhD Thesis .
•     Bib Lioutikov, R. (2018). Parsing Motion and Composing Behavior for Semi-Autonomous Manipulation , PhD Thesis .
•     Bib Veiga, F. (2018). Toward Dextrous In-Hand Manipulation through Tactile Sensing , PhD Thesis .
•       Bib Manschitz, S. (2017). Learning Sequential Skills for Robot Manipulation Tasks , PhD Thesis .
•       Bib Paraschos, A. (2017). Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives , PhD Thesis .
•     Bib Vinogradska, J. (2017). Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation , PhD Thesis .
•     Bib Calandra, R. (2016). Bayesian Modeling for Optimization and Control in Robotics , PhD Thesis .
•     Bib Daniel, C. (2016). Learning Hierarchical Policies from Human Feedback , PhD Thesis .
•     Bib Hoof, H.v. (2016). Machine Learning through Exploration for Perception-Driven Robotics , PhD Thesis .
•     Bib Kroemer, O. (2015). Machine Learning for Robot Grasping and Manipulation , PhD Thesis .
•       Bib Muelling, K. (2013). Modeling and Learning of Complex Motor Tasks: A Case Study with Robot Table Tennis , PhD Thesis .
•       Bib Wang, Z. (2013). Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Model , PhD Thesis .
•       Bib Kober, J. (2012). Learning Motor Skills: From Algorithms to Robot Experiments , PhD Thesis .
•       Bib Nguyen-Tuong, D (2011). Model Learning in Robot Control , PhD Thesis (Completed at IAS/Tuebingen before move to TU Darmstadt) .

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Game-Theoretic and Set-Based Methods for Safe Autonomous Vehicles on Shared Roads

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Improved Trajectory Planning for On-Road Self-Driving Vehicles Via Combined Graph Search, Optimization & Topology Analysis

Trajectory planning is an important component of autonomous driving. It takes the result of route-level navigation plan and generates the motion-level commands that steer an autonomous passenger vehicle (APV). Prior work on solving this problem uses either a sampling-based or optimization-based trajectory planner, accompanied by some high-level rule generation components.

Degree Type

• Dissertation
• Electrical and Computer Engineering

Degree Name

• Doctor of Philosophy (PhD)

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• Computer Engineering
• Electrical and Electronic Engineering not elsewhere classified

Lidar-Based Scene Understanding for Autonomous Driving Using Deep Learning

• Victor Vaquero Gomez

Supervisor/s

• Francesc Moreno Noguer
• Alberto Sanfeliu Cortés

Information

• Started: 01/07/2014
• Finished: 25/02/2020

Description

With over 1.35 million fatalities related to traffic accidents worldwide, autonomous driving was foreseen at the beginning of this century as a feasible solution to improve security in our roads. Nevertheless, it is meant to disrupt our transportation paradigm, allowing to reduce congestion, pollution, and costs, while increasing the accessibility, efficiency, and reliability of the transportation for both people and goods. Although some advances have gradually been transferred into commercial vehicles in the way of Advanced Driving Assistance Systems (ADAS) such as adaptive cruise control, blind spot detection or automatic parking, however, the technology is far from mature. A full understanding of the scene is necessary in order to allow the vehicles to be aware of their surroundings and the existing elements on the scene, as well as their respective motions, intentions and interactions. In this PhD dissertation, we explore new approaches for understanding driving scenes from 3D LiDAR point clouds by using Deep Learning methods. To this end, in Part I we analyze the scene from a static perspective using independent frames to detect the neighboring vehicles. Next, in Part II we develop new ways for understanding the dynamics of the scene. Finally, in Part III we apply all the developed methods to accomplish higher level challenges such as segmenting moving obstacles while obtaining their rigid motion vector over the ground. More specifically, in Chapter 2 we develop a 3D vehicle detection pipeline based on a multi-branch deep-learning architecture and propose a Front (FR-V) and a Bird’s Eye view (BE-V) as 2D representations of the 3D point cloud to serve as input for training our models. Later on, in Chapter 3 we apply and further test this method on two real uses-cases, for pre-filtering moving obstacles while creating maps to better localize ourselves on subsequent days, as well as for vehicle tracking. From the dynamic perspective, in Chapter 4 we learn from the 3D point cloud a novel dynamic feature that resembles optical flow from RGB images. For that, we develop a new approach to leverage RGB optical flow as pseudo ground truth for training purposes but allowing the use of only 3D LiDAR data at inference time. Additionally, in Chapter 5 we explore the benefits of combining classification and regression learning problems to face the optical flow estimation task in a joint coarse-and-fine manner. Lastly, in Chapter 6 we gather the previous methods and demonstrate that with these independent tasks we can guide the learning of challenging higher-level problems such as segmentation and motion estimation of moving vehicles from our own moving perspective.

The work is under the scope of the following projects:

• Cargo-ANTS: Cargo handling by Automated Next generation Transportation Systems for ports and terminals ( web )
• ColRobTransp: Colaboración robots-humanos para el transporte de productos en zonas urbanas ( web )
• ROBOCOM++: Rethinking robotics for the robot companion of the future ( web )

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Beyond the Limits: The Making of MARTYKhana

Making cars safer by programming them to  drift.

To ensure safety in the widest possible range of scenarios, automated vehicles must be able to react rapidly if needed. When trying to maneuver quickly, it is critical to consider that if you rotate the car too aggressively, it can become unstable and start to spin out.

The conventional approach to dealing with this phenomemon, that underpins Electronic Stability Control (ESC) and other similar systems, is to restrict the vehicle to stay within conservative limits wherein it is always stable. This is a  trade-off : we sacrifice agility, in exchange for making it easy for an average driver - or naive automated system - to control.

One can access a much wider range of maneuvers - and therefore, avoid accidents in a larger number of scenarios - by understanding how to control a vehicle  beyond the stability limits.  Expert drivers in drifting competitions, for example, can precisely position the car while purposefully operating in unstable conditions. Automated vehicles have the opportunity - and the responsibility - to be much better than the average driver, and thereby remove the need for this trade-off.

The Stanford News article,  ‘Automated Vehicle Control Beyond the Stability Limits’ , describes the lab’s recent contributions to controlling automated vehicles in these unstable situations. The highlight is a video that shows MARTY - the lab’s electric DeLorean - autonomously drifting through a challenging kilometer-long course.

To accompany the article, this page highlights information about the lab’s automated drifting research, the MARTY test vehicle, and additional media resources.Additional Uncut Video Footage

To accompany the videos from the article, uncut video footage from a complete run of this course is presented from three different perspectives: aerial, interior, and a camera mounted on the roof of the car.

Additional Uncut Video Footage

Aerial View

Interior View

Roof Mounted Camera View

If you're going to build an autonomous drifting car, why not do it with some style?

MARTY is a 1981 DMC DeLorean that has been extensively modified to serve as a flexible testbed for automated control at and beyond the limits of handling.

More detailed information on the build is available here.

The PhD thesis  Automated Vehicle Control Beyond the Stability Limits  by Jonathan Goh describes this research in detail, including the experiments on MARTY. The full manuscript is publicly available through the  Stanford Library .

The locations of the cones that demarcate the course are shown here, in relative East-North coordinates. This map is also available as a vector  PDF .

The cones are divided into three categories: inside of the turn, outside of the turn, and transition cones. The transition cones are closer to the centerline of the vehicle path, and represent a narrower corridor that the vehicle should pass through at low sideslip; these are represented by haybales in the video. The relative East-North coordinates of the  outside ,  inside , and  transition cones  are also available in .CSV format.

Towards Automated Vehicle Control Beyond the Stability Limits: Drifting Along a General Path

• Vehicle Dynamics and Control At The Limits of Handling

A Controller for Automated Drifting Along Complex Trajectories

• Conference Proceeding

Simultaneous Stabilization and Tracking of Basic Automobile Drifting Trajectories

MARTY Drifts Figure 8s

The Dynamic Design Lab's autonomous electric DeLorean, MARTY, repeatedly executes a highly dynamic figure 8 drifting maneuver. During the rapid transitions between +/- 40 degrees of drift angle, the vehicle steers lock-to-lock in about a second, reaching yaw rates as high as 120 deg/s. By studying how to control a vehicle in these extreme situations, the researchers are helping to make autonomous vehicles safer.

MARTY 'Cassette-Tape' Autonomous Drifting Test

This video of MARTY doing a 'cassette-tape' maneuver was first presented at the AVEC conference in Beijing, on July 17, 2018.

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MARTY with Doors Up at Thunderhill

MARTY Drifts on the Thunderhill Skidpad

MARTY Large Figure 8 Photocomposite

Sunset View of MARTY at Thunderhill

MARTY Systems

MARTY drifts on the Thunderhill skidpad

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‘Augmenting’ the doctoral thesis in preparation for a viva

The viva voce exam is the final hurdle for PhD students, but for most it is also a new and fear-inducing experience. Edward Mills offers one framework to help those preparing to discuss their completed thesis at length

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Using non verbal cues to build rapport with students, emotionally challenging research and researcher well-being, augmenting the doctoral thesis in preparation for a viva, how hard can it be testing ai detection tools.

In many ways, my own PhD viva voce examination was shaped by when and where it took place. Because I was examined at a UK institution, mine was not a public event; it was held virtually, thanks to the coronavirus pandemic; and, perhaps most depressingly, I didn’t get to wield a sword at the subsequent graduation ceremony (although my fiancée did make me a small wooden one).

Many parts of the viva, though, will be familiar to PhD candidates the world over from almost any discipline. After working independently for four years to produce an 80,000-word thesis, I was suddenly expected to discuss my work in depth, with two examiners (one from my institution, and one from elsewhere) and an independent chair present. During that time, the examiners would be checking whether my thesis was indeed my own work, and whether it met the criteria for the award of a PhD.

Understanding the ‘whole thesis’

Like many PhD students, I’d spoken about my research over the previous three years at conferences, but these presentations had largely been confined to individual chapters. Now, though, I had to become familiar not just with (say) my arguments on medieval calendars, but also on how they fitted into my broader narrative about language use in medieval England.

The approach that I took – which formed part of a suite of resources for postgraduate researchers produced by the University of Exeter’s Doctoral College – was based around what I called “augmenting” my thesis. Intimidating as this may sound, it was based around a fundamentally simple concept: turning my thesis from a lengthy PDF file into something physical and tangible and which would be of use to me during the viva.

There is, of course, no single “right way” to do this, but for the sake of clarity, and at the risk of sounding like a 1980s Blue Peter presenter , I’ll outline my own process in a series of numbered steps for the benefit of readers who may be approaching the viva themselves.

• Resource collection: Resources on academic writing
• Viving la viva: how to answer viva questions
• How to write a PhD thesis: a step-by-step guide

An ‘augmented’ thesis in four steps

Print out and bind your thesis. This would form the basis of the “object” that I would eventually take into my viva, but it also has the advantage of getting you away from a screen, making you less likely to skip over certain passages as you reread it.

As you reread, place sticky markers along the top of the thesis to coincide with chapter headings and subheadings. At each point, write a one-sentence summary of that section. These big-picture notes give a bite-sized summary of your argument in each section, and when strung together, they provide you with a sort of “thesis-on-a-page”.

When you’ve reminded yourself of how all of your arguments fit together, start to look for points of detail. This is where highlighting can be at its most useful, if done selectively: I used yellow for material that I thought was central to my argument (and that I wanted to be able to quote back to my examiners) and red for material that I felt, on reflection, would benefit from further explanation. Any sticky notes can be placed along the outer margins of the thesis, which will distinguish them from the summaries along the top.

Record typos separately. However hard you try, typographical errors will find a way into the thesis that you submit. Highlighting each individual one, however, is likely to take more time than it’s worth: instead, I’d advise making a list of typos, keyed to page numbers and suggested changes, separately: this could later form the basis of a table of corrections to be submitted to the examiners.

There are, of course, plenty of other ways in which a thesis might be augmented: one of the main themes that emerged from collaborating on Researcher Development was that doctoral research is shaped by the researcher and their own experience just as much by field and topic. A PhD thesis may have a completely different structure to the one alluded to above; it may require more or less context for an oral examination; it may (whisper it) have fewer typos than mine did. Nevertheless, finding some form of structure in the isolating and stressful months and weeks prior to the viva is an absolute necessity for doctoral researchers, and producing an augmented thesis might just be the way to achieve it.

Edward Mills is a postdoctoral research fellow at the University of Exeter. 

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