APPLICATION OF PARAMETER ESTIMATION AND CALIBRATION METHOD FOR CAR-FOLLOWING MODELS

Both safety and the capacity of the roadway system are highly dependent on the car-following characteristics of drivers. Car-following theory describes the driver behavior of vehicles following other vehicles in a traffic stream. In the last few decades, many car-following models have been developed; however, studies are still needed to improve their accuracy and reliability. Car-following models are a vital component of traffic simulation tools that attempt to mimic driver behavior in the real world. Microscopic traffic simulators, particularly car-following models, have been extensively used in current traffic engineering studies and safety research. These models are a vital component of traffic simulation tools that attempt to mimic real-world driver behaviors. The accuracy and reliability of microscopic traffic simulation models are greatly dependent on the calibration of car-following models, which requires a large amount of real world vehicle trajectory data. In this study, the author developed a process to apply a stochastic calibration method with appropriate regularization to estimate the distribution of parameters for car-following models. The calibration method is based on the Markov Chain Monte Carlo (MCMC) simulation using the Bayesian estimation theory that has been recently investigated for use in inverse problems. This dissertation research includes a case study, which is based on the Linear (Helly) model with a different number of vehicle trajectories in a highway network. The stochastic approach facilitated the calibration of car-following models more realistically than the deterministic method, as the deterministic algorithm can easily get stuck at a local minimum. This study also demonstrates that the calibrated model yields smaller errors with large sample sizes. Furthermore, the results from the Linear model validation effort suggest that the performance of the calibration method is dependent upon size of the vehicle trajectory

A Highway-Driving System Design Viewpoint using an Agent-based Modeling of an Affordance-based Finite State Automata

This paper presents an agent-based modeling framework for affordance-based driving behaviors during the exit maneuver of driver agents in human-integrated transportation problems. We start our discussion from one novel modeling framework based on the concept of affordance called the Affordance-based Finite State Automata (AFSA) model, which incorporates the human perception of resource availability and action capability. Then, the agent-based simulation illustrates the validity of the AFSA framework for the Highway-Lane-Driver System. Next, the comparative study between real driving data and agent-based simulation outputs is provided using the transition diagram. Finally, we perform a statistical analysis and a correlation study to analyze affordance-based driving behavior of driver agents. The simulation results show that the AFSA model well represents the perception-based human actions and drivers??? characteristics, which are essential for the design viewpoint of control framework of human driver modeling. This study is also expected to benefit a designed control for autonomous/self-driving car in the future

An ensemble deep learning approach for driver lane change intention inference

With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts

Turn Detection and Analysis of Turn Parameters for Driver Characterization

Advanced Driver Assistance Systems, or ADAS, which can notify the driver of potential dangers or even perform emergency maneuvers in dangerous situations, have been shown to play a crucial role in accident prevention and driver feedback. An Intelligent ADAS, or i-ADAS, relies on information about the state of the driver, their behavior or condition, the vehicle and the environment. Understanding the behavior requires the development of driver models, which can help predict how a person may react in certain situations or help determine if the individual is not performing at their usual level of ability. A key element in building such models is the ability to detect and analyze common driving maneuvers, such as making turns, on an individual-by-individual basis. Thus algorithms are needed which can detect and characterize individual driving maneuvers. In this research, we present a position-based turn detection algorithm for detecting turns from vehicle data and GPS coordinates. Based on a dataset of sixteen drivers involving 278 turns, the algorithm achieves an accuracy of 97.84%. The turn parameters detected by the algorithm are then averaged for each driver and clustered using K-Means. Turn parameters t - 5 seconds are also clustered prior to each detected turn and t + 5 seconds are clustered after each turn. The cluster centroids at each point in time determine particular driving behaviours which are summarized in four categories, and the cluster assignments are examined over time to categorize drivers into these behaviour categories. This analysis reveals two optimal times for analyzing driver behaviour. Our overall aim is to be able to build automated methods that can use this research to eventually determine characteristics of individual drivers during turns in order to build models of drivers for use with i-ADAS

A Model Based Approach to the Analysis of Intersection Conflicts and Collision Avoidance Systems

This dissertation studies the viability of driver assistance systems to improve the safety of „unprotected‟ left turns at signalized intersections. To achieve this, modeling and simulation have been conducted, including a driver model, with calibration and validation based on naturalistic driving data. A detailed analysis of the driving data has been conducted to reconstruct the vehicle trajectories in an automated manner. Particular challenges for this analysis include the development of automated detection of relevant events in a large database, automated estimation of sensor latencies, and the multiple application of Kalman filtering to fuse motion variables. A conflict analysis has been conducted to estimate the actual and predicted available gaps using the reconstructed vehicle trajectories. Monte Carlo simulations were conducted to create a large number of free left turn events in order to simulate a proposed driver assistance system and optimize safety performance. Optimization was conducted using multiobjective techniques which balance performance in terms of the rates of correct detections of conflicts, false alarms, and successful braking under the condition of correct detections based on Pareto optimality criteria. In this study, data to support the analysis was obtained from onboard instrumentation, where it was found essential to include detailed estimation of latencies between various sensors; after this, data fusion can be performed. It was found that high fidelity modeling of longitudinal control is critical to the safety system analysis. Also, it was found necessary to represent multiple levels of control, including visual preview and acceleration feedback. For the speed control reference, it was found that an “anticipated acceleration” can be used to define both straight braking events and free left turns; the driver may keep both options available during the intersection approach up to a critical decision point where the two references are equal. It was critical to the parametric optimization of the driver assistance system to take account of the need for warnings to be issued sufficiently early for the driver to respond; multiobjective design optimization was found to be an appropriate tool to include this requirement, as well as more typical requirements for involving false warnings.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89791/1/knobukaw_1.pd

Objectification of willingness to cooperate using the example of a lane change in the low speed range

Die zunehmende Automatisierung der Fahrzeuge verspricht viele Vorteile, wie die Reduktion von Emissionen oder einen erhöhten Komfort. Die Erwartungen der Bevölkerung an das automatisierte Fahren sind hoch. Demgegenüber stehen Ängste. Eine Herausforderung, die gerade in der Übergangsphase zum autonomen Fahren im Vordergrund steht, ist die Kommunikation im Mischverkehr zwischen menschlichen und automatisierten Fahrzeugen. Hierfür muss eine Lösung gefunden werden, damit das automatisierte Fahrzeug von den umgebenden Verkehrsteilnehmern verstanden und akzeptiert wird. Die Frage über den Umgang mit derartigen Situationen wurde bereits von mehreren Autoren aufgeworfen. Lösungsvorschläge in der Entwicklung von Fahrstrategien gehen in Richtung des kooperativen Fahrens, beleuchten dabei aber die psychologische Komponente zu wenig, zum einen die informelle aktive Kommunikation zwischen Menschen, zum anderen auch die Attribution während der Interaktion. Die vorliegende Arbeit liefert hierfür einen interdisziplinären Ansatz. Ziel ist es Handlungsempfehlungen für die Gestaltung einer verhaltensbasierten Fahrstrategie basierend auf psychologischen Erkenntnissen und Theorien geben. Ein Anwendungsbeispiel, das zu den komplexesten Szenarien auf der Autobahn gehört und ein erhöhtes Maß an koordinierter Abstimmung erfordert ist ein Fahrstreifenwechselszenario im Niedriggeschwindigkeitsbereich auf der Autobahn. Dieser wurde aus Einscherer- und Folgefahrerperspektive in drei Studien im Fahrsimulator beleuchtet. Im ersten Schritt wurde untersucht wie Menschen in einer derartigen Situation miteinander kommunizieren und wie dies jeweils aus beiden Perspektiven wahrgenommen wurde. Dabei wurde genauer analysiert, was in diesem Kontext eine eindeutige und kooperative Kommunikation ausmacht und wie dies anhand von Fahrparametern objektiviert werden kann.In einem zweiten Schritt wurden die Ergebnisse für den betrachteten Anwendungsfall in Form eines neuen Brems- und Spurwechselalgorithmus in die bestehende Verkehrssimulation integriert und mit den Ursprungsstrategien im Fahrsimulator verglichen. Diesmal lag der Fokus auf der Interaktion zwischen menschlichen und autonomen Fahrzeugen. Es zeigte sich, dass die neu entwickelten Strategien besser bewertet wurden. Die Studien unterstreichen die Wichtigkeit und Notwendigkeit psychologische Aspekte bei der Entwicklung von Algorithmen mit zu beachten, um eine höhere Verkehrssicherheit und Akzeptanz in der Bevölkerung zu schaffen.The increasing automation of vehicles promises many advantages, such as reduction of emissions or increased comfort. The public's expectations of automated driving are high. However, concerns and fears still remain. One challenge that is particularly important in the transition phase to autonomous driving is communication in mixed traffic between human and automated vehicles. A solution must be found to this challenge so that the automated vehicle is understood and accepted by the surrounding road users. The question of how to deal with such situations has already been raised by several authors. Suggestions for solutions in the development of driving strategies go in the direction of cooperative driving, but do not shed enough light on the psychological component, concerning the informal active communication between people, but also the attribution during the interaction. This paper provides an interdisciplinary approach. The aim is to give recommendations for action for the design of a behavior oriented driving strategy based on psychological findings and theories. One use case that is one of the most complex scenarios on the highway and requires an increased degree of coordinated alignment between drivers, is a lane change scenario in a low speed range. This was examined in three studies in the driving simulator from a merging and car following perspective. The first step was to investigate how people communicate with each other in such a situation and how this was perceived from the perspective of the cut in vehicle and the lag driver. In this context, it was analyzed in more detail what constitutes unambiguous and cooperative communication and how this can be objectified on the basis of driving parameters. In a second step, the results for the application in question were integrated into the existing traffic simulation in the form of a new brake and lane change algorithm and compared with the original strategies in the driving simulator. This time the focus was on the interaction between human and autonomous vehicles. It turned out that the newly developed strategies were better evaluated. These studies underline the importance and necessity to consider psychological aspects in the development of algorithms in order to create higher traffic safety and acceptance in the population

Probabilistische Vorhersage von Fahrstreifenwechseln für hochautomatisiertes Fahren auf Autobahnen

Die vorliegende Arbeit stellt ein Konzept zur zeitlichen Vorhersage von Fahrstreifenwechselmanövern auf Autobahnen für Systeme zur automatischen Fahrzeugführung vor. Derartige Systeme benötigen ein Verständnis der Fahrumgebung zur konfliktfreien und nachvollziehbaren Durchführung der Fahraufgabe. Dies beinhaltet die Wahrnehmung und Interpretation der Fahrumgebung zur Erkennung und Vorhersage von Fahrmanövern des umgebenden Verkehrs

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

Lane-Change Detection Using a Computational Driver Model

Objective: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. Method: Using a “model tracing ” methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model’s simulated behavior with a driver’s actual observed behavior and thus continually infers the driver’s unobservable intentions from her or his observable actions. Results: For data collected in a driving simulator, the system detects 82 % of lane changes within 0.5 s of maneuver onset (assuming a 5 % false alarm rate), 93 % within 1 s, and 95 % before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61 % within 0.5 s, 77 % within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. Conclusion: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. Application: By providing robust realtime detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems