autonomous car phd thesis

Motion Planning for Autonomous Vehicles in Urban Scenarios: A Sequential Optimization Approach

Degree type.

• Dissertation
• Electrical and Computer Engineering

Degree Name

• Doctor of Philosophy (PhD)

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• Control Systems, Robotics and Automation

Trajectory Planning and Control for an Autonomous Race Vehicle

Autonomous vehicle technologies offer potential to eliminate the number of traffic accidents that occur every year, not only saving numerous lives but mitigating the costly economic and social impact of automobile related accidents. The premise behind this dissertation is that autonomous cars of the near future can only achieve this ambitious goal by obtaining the capability to successfully maneuver in friction-limited situations. With automobile racing as an inspiration, this dissertation presents and experimentally validates three vital components for driving at the limits of tire friction. The first contribution is a feedback-feedforward steering algorithm that enables an autonomous vehicle to accurately follow a specified trajectory at the friction limits while preserving robust stability margins. The second contribution is a trajectory generation algorithm that leverages the computational speed of convex optimization to rapidly generate both a longitudinal speed profile and lateral curvature profile for the autonomous vehicle to follow. The final contribution is a set of iterative learning control and search algorithms that enable autonomous vehicles to drive more effectively by learning from previous driving maneuvers. These contributions enable an autonomous Audi TTS test vehicle to drive around a race circuit at a level of performance comparable to a professional human driver. The dissertation concludes with a discussion of how the algorithms presented can be translated into automotive safety systems in the near future.

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Related Topics

• Motion Planning for Autonomous Vehicles
• Vehicle Dynamics and Control At The Limits of Handling

More Publications

Contingency model predictive control for automated vehicles, insights into vehicle trajectories at the handling limits: analysing open data from race car drivers, uber's dilemma: how the ada may end the on-demand economy.

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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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Towards pedestrian-aware autonomous cars

• Group Van de Molengraft
• Control Systems Technology

Research output : Thesis › Phd Thesis 1 (Research TU/e / Graduation TU/e)

Bibliographical note

Access to document.

• 20211201_Dolatabadi_Farahani Final published version, 45.7 MB

T1 - Towards pedestrian-aware autonomous cars

AU - Dolatabadi, Marzieh

N1 - Proefschrift.

PY - 2021/12/1

Y1 - 2021/12/1

M3 - Phd Thesis 1 (Research TU/e / Graduation TU/e)

SN - 978-90-386-5425-6

PB - Eindhoven University of Technology

CY - Eindhoven

The impacts of car-sharing and shared autonomous vehicles on urban mobility: Towards a sustainable future

Zhou, Fan (2018) The impacts of car-sharing and shared autonomous vehicles on urban mobility: Towards a sustainable future. PhD thesis, Queensland University of Technology.

Description

This dissertation presents a big-picture view for policymakers and related stakeholders regarding the future development of car-sharing services. Car sharing has the potential to significantly disrupt the personal mobility market, particularly on the dawn of self-driving cars. Thus, understanding car-sharing service's market penetration and implications are urgently needed. Studies in this thesis aim to forecast the penetration of car-sharing, to investigate if car-sharing influence consumers' vehicle ownership decision, and to explore the impacts of car-sharing on households' mode choice decision.

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Deep learning-based scene understanding for autonomous vehicles

• Hafsa Taher

Supervisor/s

• Angel Santamaria Navarro

Information

• Started: 01/01/2023

Description

The increasing demand for autonomous vehicles has led to a growing need for robust and reliable methods for interpreting the visual information captured by the vehicle's sensors. One key technique for achieving this goal is semantic segmentation, which involves assigning a semantic label or class to each pixel in an image. The goal of this PhD research is to develop and evaluate novel methods for semantic segmentation of images for autonomous vehicles. The research will focus on three main areas: * Developing new deep learning-based algorithms for semantic segmentation that are specifically tailored to the unique challenges of autonomous vehicle applications, such as handling large amounts of data, dealing with complex and dynamic scenes, and coping with limited computational resources. * Evaluating the performance of the developed algorithms on large-scale datasets of real-world images captured by cameras and lidar sensors mounted on autonomous vehicles. This will involve using metrics such as accuracy, precision, recall, and F1-score to quantify the performance of the algorithms. * Investigating the impact of semantic segmentation on the overall performance of autonomous vehicles by integrating the developed algorithms into a simulation environment and in real robotic hardware; and evaluating the vehicle's ability to navigate safely and efficiently. Overall, this PhD research aims to contribute to the development of safer and more reliable autonomous vehicles by providing a better understanding of how to use semantic segmentation to interpret the visual information captured by the vehicle's sensors.

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Dzerzhinsky in Moscow Oblast Destination Guide Russia

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Dzerzhinsky in Moscow Oblast, Russia

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Dzerzhinsky in Moscow Oblast is located in Russia about 13 mi (or 21 km) south-east of Moscow, the country's capital.

Local time in Dzerzhinsky is now 08:16 PM (Monday). The local timezone is named Europe / Moscow with an UTC offset of 3 hours. We know of 7 airports in the wider vicinity of Dzerzhinsky, of which two are larger airports. The closest airport in Russia is Bykovo Airport in a distance of 8 mi (or 13 km), East. Besides the airports, there are other travel options available (check left side).

There are several Unesco world heritage sites nearby. The closest heritage site in Russia is Church of the Ascension, Kolomenskoye in a distance of 7 mi (or 12 km), West. If you need a place to sleep, we compiled a list of available hotels close to the map centre further down the page.

Depending on your travel schedule, you might want to pay a visit to some of the following locations: Orekhovo-Borisovo Yuzhnoye, Moscow, Cheremushki, Ramenskoye and Vostochnoe Degunino. To further explore this place, just scroll down and browse the available info.

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Ugresha Monastery

Nikolo-Ugreshsky Monastery is a walled stauropegic Russian Orthodox monastery of St. Nicholas the Miracle-Worker located in a suburb of Moscow formerly known as Ugreshi and now called Dzerzhinsky. It is the town's main landmark and is featured on the . The monastery is known to have existed as early as 1521, when the Tatar horde of Mehmed I Giray reduced Ugreshi to ashes. The old katholikon of St. Nicholas (later destroyed by the Soviets) was built in the 16th century.

Located at 55.6217, 37.84 (Lat. / Lng.), about 1 miles away. Wikipedia Article Russian Orthodox monasteries, Buildings and structures in Moscow Oblast, Christian monasteries established in the 14th century, Museums in Moscow Oblast, Religious museums in Russia, Biographical museums in Russia, Decorative arts museums in Russia

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Elektrostal

Elektrostal Localisation : Country Russia , Oblast Moscow Oblast . Available Information : Geographical coordinates , Population, Area, Altitude, Weather and Hotel . Nearby cities and villages : Noginsk , Pavlovsky Posad and Staraya Kupavna .

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