Data-Driven Methods and Applications for Optimization under Uncertainty and Rare-Event Simulation

For most of decisions or system designs in practice, there exist chances of severe hazards or system failures that can be catastrophic. The occurrence of such hazards is usually uncertain, and hence it is important to measure and analyze the associated risks. As a powerful tool for estimating risks, rare-event simulation techniques are used to improve the efficiency of the estimation when the risk occurs with an extremely small probability. Furthermore, one can utilize the risk measurements to achieve better decisions or designs. This can be achieved by modeling the task into a chance constrained optimization problem, which optimizes an objective with a controlled risk level. However, recent problems in practice have become more data-driven and hence brought new challenges to the existing literature in these two domains. In this dissertation, we will discuss challenges and remedies in data-driven problems for rare-event simulation and chance constrained problems. We propose a robust optimization based framework for approaching chance constrained optimization problems under a data-driven setting. We also analyze the impact of tail uncertainty in data-driven rare-event simulation tasks. On the other hand, due to recent breakthroughs in machine learning techniques, the development of intelligent physical systems, e.g. autonomous vehicles, have been actively investigated. Since these systems can cause catastrophes to public safety, the evaluation of their machine learning components and system performance is crucial. This dissertation will cover problems arising in the evaluation of such systems. We propose an importance sampling scheme for estimating rare events defined by machine learning predictors. Lastly, we discuss an application project in evaluating the safety of autonomous vehicle driving algorithms.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163270/1/zhyhuang_1.pd

Computational driver behavior models for vehicle safety applications

The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. Special attention is paid to driver glance behavior in critical situations and the role of peripheral vision.First, a hybrid framework based on autoregressive models with exogenous input (ARX-models) is employed to predict and classify driver control in real time. Two models are suggested, one targeting steering behavior and the other longitudinal control behavior. Although the predictive performance is unsatisfactory, both models can distinguish between different driving styles.Moreover, a basic model for drivers\u27 brake initiation and modulation in critical longitudinal situations (specifically for rear-end conflicts) is constructed. The model is based on a conceptual framework of noisy evidence accumulation and predictive processing. Several model extensions related to gaze behavior are also proposed and successfully fitted to real-world crashes and near-crashes. The influence of gaze direction is further explored in a driving simulator study, showing glance response times to be independent of the glance\u27s visual eccentricity, while brake response times increase for larger gaze angles, as does the rate of missed target detections.Finally, the potential of a set of metrics to quantify subjectively perceived risk in lane departure situations to explain drivers\u27 recovery steering maneuvers was investigated. The most influential factors were the relative yaw angle and splay angle error at steering initiation. Surprisingly, it was observed that drivers often initiated the recovery steering maneuver while looking off-road.To sum up, the proposed models in this thesis facilitate the development of personalized ADASs and contribute to trustworthy virtual evaluations of current, future, and conceptual safety systems. The insights and ideas contribute to an enhanced, human-centric system development, verification, and validation process. In the long term, this will likely lead to improved vehicle safety and a reduced number of severe injuries and fatalities in traffic

PRE-CRASH EXTRACTION OF THE CONSTELLATION OF A FRONTAL COLLISION BETWEEN TWO MOTOR VEHICLES

One of the strategic objectives of the European Commission is to halve the number of road traffic fatalities by 2020. In addition, in 2010, the United Nations General Assembly initiated the "Decade of Action for Road Safety 2011-2020" to reduce the number of fatalities and decrease the number of road traffic injuries. To address the scourge of road traffic accidents, this thesis presents a research study which has devised and evaluated a novel algorithm for extracting the constellation of an unavoidable frontal vehicle-to-vehicle accident. The primary research questions addressed in this work are: • What are the most significant collision parameters which influence the injury severity for a frontal collision between two motor vehicles? • How to extract the constellation of a crash before the accident occurs? In addition, the secondary research questions given below were addressed: • How to integrate physical constraints, imposed on the rate of acceleration of a real vehicle, together with data from vehicle-to-vehicle (V2V) communication, into the crash constellation extraction algorithm? • How to integrate uncertainties, associated with the data captured by sensors of a real vehicle, into a simulation model devised for assessing the performance of crash constellation extraction algorithms? Statistical analysis, conducted to determine significant collision parameters, has identified three significant crash constellation parameters: the point of collision on the vehicle body and the relative velocity between the vehicles; and the vehicle alignment offset (or vehicle overlap). The research reported in this thesis has also produced a novel algorithm for analysing the data captured by vehicle sensors, to extract the constellation of an unavoidable vehicle-to-vehicle frontal accident. The algorithm includes a model of physical constraints on the acceleration of a vehicle, cast as a gradual rise and eventual saturation of vehicle acceleration, together with the acceleration lag relative to the timing of information received from V2V communication. In addition, the research has delivered a simulation model to support the evaluation of the performance of crash constellation extraction algorithms, including a technique for integrating (into the simulation model, so that the simulation can approach real-world behaviour) the uncertainties associated with the data captured by the sensors of a real vehicle. The results of the assessment of the soundness of the simulation model show that the model produces the expected level of estimation errors, when simulation data is considered on its own or when it is compared to data from tests performed with a real vehicle. Simulation experiments, for the performance evaluation of the crash constellation extraction algorithm, show that the uncertainty associated with the estimated time-to-collision decreases as vehicle velocity increases or as the actual time-to-collision decreases. The results also show that a decreasing time-to-collision leads to a decreasing uncertainty associated with the estimated position of the tracked vehicle, the estimated collision point on the ego vehicle, and the estimated relative velocity between the two vehicles about to collide. The results of the performance assessment of the crash constellation extraction algorithm also show that V2V information has a beneficial influence on the precision of the constellation extraction, with regards to the predicted time-to-collision, the predicted position and velocity of the oncoming vehicle against which a collision is possible; the predicted relative velocity between the two vehicles about to collide, and the predicted point of collision on the body of the ego vehicle. It is envisaged that the techniques, developed in the research reported in this thesis, will be used in future integrated safety systems for motor vehicles. They could then strongly impact passenger safety by enabling optimal activation of safety measures to protect the vehicle occupants, as determined from the estimated constellation of the impending crash