No description available.

Most safety analysis is conducted using crash data. Surrogate safety measures, such as various time-based measures of time-to-collision can be related to crash potential and used to gain insight into the frequency and severity of crashes at a specific location. One of the most common and acknowledged measures is post-encroachment time (PET) which defines the time between vehicles occupying a conflicting space. While commonly used in studies of motor vehicle interactions, studies of PET for bicycle-vehicle interactions are few. In this research, the PET of bicycle-vehicle interactions measured in the field, a driving simulator, and in a micro-simulation are compared. A total of 52 right-hook conflicts were identified in 135 hours of video footage over 14 days at a signalized intersection in Portland, OR (SW Taylor and SW Naito Pkwy). The results showed that 4 of 17 high-risk conflicts could not be identified by the conventional definition of PET and PET values of some conflicts did not reflect true risk of collision. Therefore, right-hook conflicts were categorized into two types and a modified measure of PET was proposed so that their frequency and severity were properly measured. PETs from the field were then compared to those measures in the Oregon State University driving simulator during research conducted by Dr. Hurwitz et al. (2015) studying the right-hook conflicts. Statistical and graphical methods were used to compare field PETs to those in the simulator. The results suggest that the relative validity of the OSU driving simulator was good but not conclusive due to differences in traffic conditions and intersections. To further explore the field-observed PET values, traffic simulation models of the field intersection were developed and calibrated. Right-hook conflicts were extracted from the simulation files and conflicts observed in PM-peak hours over 6 days in the field were compared to those obtained from 24 traffic simulation runs. The field-observed PET values did not match the values from the simulation values very well. However, the approach does show promise. Further calibration of driving and bicycling behaviors would likely improve the result.

No description available.

Thesis (Ph. D.)--University of Adelaide, 2004. / Title from title screen. Description based on contents viewed Apr. 21, 2005. Includes bibliographical references.

Learning human driving behaviors is a promising approach to enhance the performance of self-driving vehicles. By understanding and replicating the complex decision-making processes of human drivers, developers are able to program vehicles to navigate real-world scenarios with better safety and reliability. This strategy not only improves the adaptability of autonomous driving systems but also ensures their capability to manage unexpected situations on the road. Traditional Imitation Learning (IL) methods have been a cornerstone in achieving this objective, which typically assume that the expert demonstrations follow Markov Decision Processes (MDPs). However, in reality, this assumption does not always hold true. Spurious correlation may exist through the paths of historical variables because of the existence of unobserved confounders. Additionally, agents may differ in their sensory capabilities, meaning that some of the expert's features might not always be observed by the imitator. Accounting for the latent causal relationships from unobserved variables to outcomes, this dissertation focuses on Causal Imitation Learning for learning driver behaviors. First of all, this dissertation develops a sequential causal template that generalizes the default MDP settings to one with Unobserved Confounders (MDPUC-HD). Based on it, a sufficient graphical criterion is developed to determine when ignoring causality leads to poor performances in MDPUC-HD. Through the framework of Adversarial Imitation Learning (AIL), a procedure is developed to imitate the expert policy by blocking 𝜋-backdoor paths at each time step. The proposed methods are evaluated on a synthetic dataset and a real-world highway driving dataset (NGSIM), both demonstrating that the proposed procedure significantly outperforms non-causal imitation learning methods. Generalizing the findings across various graphical settings, this dissertation further proposes novel graphical conditions that allow the imitator to learn a policy performing as well as the expert's behavior policy, even when the imitator and the expert's state-action space disagree, and unobserved confounders (UCs) are generally present. When provided with parametric knowledge about the unknown reward function, such a policy is able to outperform expert performance. Additionally, our method is easily extensible with the existing IRL algorithms, including the multiplicative-weights algorithm (MWAL) and the generative adversarial imitation learning (GAIL), enhancing their adaptability to diverse conditions. The validity of the framework has been rigorously tested through extensive experiments, covering different dimensions of the causal imitation learning tasks, including: different causal assumptions, parametric families of reward functions, and multiple datasets, and infinite horizons. The results consistently affirm the superiority of the causal imitation learning approach over traditional methods, particularly in environments with unobserved confounders and different input covariate spaces.

Call number: LD2668 .T4 1986 G35 / Master of Science / Industrial and Manufacturing Systems Engineering

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Chan, Kai Chi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 91-98). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Literature Review --- p.7 / Chapter 1.3.1 --- Headlamp Preference Investigation --- p.7 / Chapter 1.3.2 --- Vehicle Dynamic System --- p.10 / Chapter 1.3.3 --- Inertial Navigation Systems --- p.11 / Chapter 1.4 --- Objective --- p.12 / Chapter 1.5 --- A Sensor Based Method --- p.14 / Chapter 1.5.1 --- Accelerometer --- p.14 / Chapter 1.5.2 --- Lighting --- p.16 / Chapter 1.5.3 --- System Design --- p.19 / Chapter 1.6 --- Thesis Organization --- p.20 / Chapter 1.7 --- Achievement and Contributions --- p.21 / Chapter 2 --- Methodology --- p.23 / Chapter 2.1 --- Kinematics of a Turning Car --- p.24 / Chapter 2.2 --- Headlamp Direction Prediction --- p.27 / Chapter 2.2.1 --- Steering Wheel Angle Measurement --- p.28 / Chapter 2.2.2 --- Steering Wheel Angle Stabilization --- p.31 / Chapter 2.2.3 --- Auxiliary Headlamps Control --- p.36 / Chapter 3 --- Implementation --- p.48 / Chapter 3.1 --- Hardware Configuration --- p.49 / Chapter 3.2 --- Design Framework --- p.51 / Chapter 3.3 --- Night Drive Simulator --- p.55 / Chapter 3.3.1 --- Simulator Configuration --- p.56 / Chapter 3.3.2 --- Turning Path Prediction --- p.61 / Chapter 3.3.3 --- Auxiliary Headlamps Control --- p.63 / Chapter 4 --- Experiments --- p.65 / Chapter 4.1 --- Steering Wheel Angle Measurement --- p.66 / Chapter 4.1.1 --- Experiment Setup --- p.66 / Chapter 4.1.2 --- Evaluation Results --- p.68 / Chapter 4.2 --- Auxiliary Headlamps Prediction --- p.71 / Chapter 4.2.1 --- Simulation --- p.72 / Chapter 4.2.2 --- Test Drive --- p.76 / Chapter 5 --- Conclusions --- p.86 / Chapter 5.1 --- Summary --- p.86 / Chapter 5.2 --- Limitations and Future Works --- p.87 / Chapter 5.2.1 --- Headlamp of LEDs --- p.87 / Chapter 5.2.2 --- Simple Car Model --- p.88 / Chapter 5.2.3 --- Response Time of Filtering --- p.88 / Chapter 5.2.4 --- Test Drive --- p.89 / Chapter 6 --- Publications --- p.90 / Bibliography --- p.91

Thesis (Ph. D.)--Helsinki University of Technology, 2001. / Includes bibliographical references (p. 49-55). Also available on the World Wide Web.

Thesis (doctoral)--Göteborgs universitet, 2001. / Extra t.p. with thesis statement and English abstract inserted. Includes bibliographical references (p. 234-244).