Autonomous navigation in interaction-based environments - a case of non-signalised roundabouts

To reduce the number of collision fatalities at crossroads intersections many countries have started replacing intersections with non-signalised roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviours according to the scenario. A non-signalised roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction dependent scenarios safely, efficiently and comfortably has been a challenge even for human drivers. Unlike traffic signal controlled roundabouts where the merging order is centrally controlled, driving a non-signalised roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an Adaptive Tactical Behaviour Planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviours for navigating a non-signalised roundabout, combining naturalistic behaviour planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behaviour planning approach and ATBP design are described in the paper

Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted

Modeling users' powertrain preferences

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 79).Our goal is to construct a system that can determine a drivers preferences and goals and perform appropriate actions to aid the driver achieving his goals and improve the quality of his road behavior. Because the recommendation problem could be achieved effectively once we know the driver's intention, in this thesis, we are going to solve the problem to determine the driver's preferences. A supervised learning approach has already been applied to this problem. However, because the approach locally classify a small interval at a time and is memoryless, the supervised learning does not perform well on our goal. Instead, we need to introduce new approach which has following characteristics. First, it should consider the entire stream of measurements. Second, it should be tolerant to the environment. Third, it should be able to distinguish various intentions. In this thesis, two different approaches, Bayesian hypothesis testing and inverse reinforcement learning, will be used to classify and estimate the user's preferences. Bayesian hypothesis testing classifies the driver as one of several driving types. Assuming that the probability distributions of the features (i.e. average, standard deviation) for a short period of measurement are different among the driving types, Bayesian hypothesis testing classifies the driver as one of driving types by maintaining a belief distribution for each driving type and updating it online as more measurements are available. On the other hand, inverse reinforcement learning estimates the users' preferences as a linear combination of driving types. The inverse reinforcement learning approach assumes that the driver maximizes a reward function while driving, and his reward function is a linear combination of raw / expert features. Based on the observed trajectories of representative drivers, apprenticeship learning first calculates the reward function of each driving type with raw features, and these reward functions serve as expert features. After, with observed trajectories of a new driver, the same algorithm calculates the reward function of him, not with raw features, but with expert features, and estimates the preferences of any driver in a space of driving types.by Jongu Shin.M.Eng

Vehicle Steering Systems - Hardware-in-the-Loop Simulator, Driving Preferences, and Vehicle Intervention

The steering system is a critical component of all ground vehicles regardless of their propulsion source. Chassis directional control is provided by the steering system, which in turn relays valuable feedback about the road and vehicle behavior. As the primary feedback channel to the driver, the steering system also delivers the initial perception of a vehicle\u27s handling and responsiveness to the consumer. Consequently, the steering system is an important aspect of the vehicle\u27s evaluation and purchasing process, even if drivers are unaware of its direct influence in their decision making. With automobile purchases potentially hinging on the steering system, a need exists for a better understanding of steering preference through a focused research project. In this investigation, driver steering preferences have been studied using an advanced hardware-in-the-loop automobile steering simulator. Additionally, vehicle run-off-road situations have been studied, which occur when some of the vehicle wheels drift off the road surface and the driver recovers through steering commands. The Clemson University steering simulator underwent three significant generations of refinements to realize a state-of-the-art automotive engineering tool suitable for human subject testing. The first and third generation refinements focused on creating an immersive environment, while the second generation introduced the accurate reproduction of steering feel found in hydraulic systems and real-time adjustable steering feel. This laboratory simulator was the first known validated driving simulator developed for the sole purpose of supporting driver steering preference studies. The steering simulator successfully passed all validation tests (two pilot studies) leading to an extensive demographics-based driver preference study with 43 subjects. This study reflected the following preliminary trends: Drivers who used their vehicles for utility purposes preferred quicker steering ratios and heavier efforts in residential, country, and highway environments. In contrast, car enthusiasts preferred quick steering ratios in residential and country environments and light steering effort on the highway. Finally, rural drivers preferred quicker steering ratios on country roads. These relationships may be used to set steering targets for future vehicle developments to accurately match vehicles to their intended market segments. The second research aspect was the development of an objective steering metric to evaluate a driver\u27s steering preference. In past simulator studies, driver feedback has been gathered extensively using written questionnaires. However, this delays the testing procedure and introduces an outside influence that may skew results. Through the data collected in this project, a robust objective steering preference metric has been proposed to gather steering preferences without directly communicating with the driver. The weighted steering preference metric demonstrated an excellent correlation with survey responses of -0.39 regardless of steering setting. This global steering preference metric used a combination of yaw rate, longitudinal acceleration, and lateral acceleration. The objective data was further dissected and it was discovered that changes made to the steering ratio resulted in a correlation of -0.55 between the objective data and subjective response from the test subjects. This substantial correlation relied on the longitudinal acceleration, left front tire angle, and throttle position. Beyond steering preferences, vehicle safety remains a major concern for automotive manufacturers. One important type of crash results from the vehicle leaving the road surface and then returning abruptly due to large steering wheel inputs: road runoff and return. A subset of run-off-road crashes that involves a steep hard shoulder has been labeled \u27shoulder induced accidents\u27. An active steering controller was developed to mitigate these \u27shoulder induced accidents\u27. A cornering stiffness estimation technique, using a Kalman filter, was coupled with a full state feedback controller and \u27driver intention\u27 module to create a safe solution without excessive intervention. The concept was designed to not only work for shoulder induced accidents, but also for similar road surface fluctuations like patched ice. The vehicle crossed the centerline after 1.0s in the baseline case; the controller was able to improve this to 1.3s for a 30% improvement regardless of driver expertise level. For the case of an attentive driver, the final heading angle of the vehicle was reduced by 47% from 0.48 rad to 0.255 rad. These laboratory investigations have clearly demonstrated that advancements in driver preference and vehicle safety may be realized using simulator technology. The opportunity to apply these tools should result in better vehicles and greater safety of driver and occupants. With the development of the objective steering preference metric, future research opportunities exist. For prior steering preference research, the feedback loop has typically required interaction with the subject to rate a setting before continuing. However, the objective steering preference metric allows this step to be automated, opening the door for the development of an automatic tuning steering system

Ergonomics of intelligent vehicle braking systems

The present thesis examines the quantitative characteristics of driver braking and pedal operation and discusses the implications for the design of braking support systems for vehicles. After the current status of the relevant research is presented through a literature review, three different methods are employed to examine driver braking microscopically, supplemented by a fourth method challenging the potential to apply the results in an adaptive brake assist system. First, thirty drivers drove an instrumented vehicle for a day each. Pedal inputs were constantly monitored through force, position sensors and a video camera. Results suggested a range of normal braking inputs in terms of brake-pedal force, initial brake-pedal displacement and throttle-release (throttle-off) rate. The inter-personal and intra-personal variability on the main variables was also prominent. [Continues.

User-centred car design and the role of feedback in driving

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A survey of car manufacturers reveals an impressive list of upcoming technologies, the combined effect of which is likely to have a profound impact upon feedback to the driver. Feedback is information that the situation provides back to the driver and is specified with reference to content, source, and timing. Feedback quality is achieved when the information requirements of the task, derived from a new task analysis of driving, are matched to the sources, content, and timing of feedback provided by the environment and the vehicle. An exploratory on-road study begins by observing that better quality feedback is implicated in increasing driver's situational awareness (even though drivers have little self awareness of this fact), and optimising mental workload. The exploratory level of analysis builds into the experimental, whereby a highly controlled simulator study replicates and builds upon these findings. Feedback is again seen to positively influence situational awareness, where changes in driver's confidence ratings as to the presence or absence of feedback information in the simulation were observed, according to the modality of feedback presented. This was achieved with a probe recall paradigm, and using psychophysical techniques as a useful extension to the Situational awareness Global Assessment Technique (SAGAI). Similarly, an analysis of mental workload via the NASA TLX self report questionnaire demonstrates that a combination of visual, steering force feedback and auditory feedback gives rise to lower mental workload, lower driver frustration, and lower, though possibly more realistic self ratings of performance. This knowledge can be discussed with reference to a feedback framework of driving that provides the theoretical backdrop to the key psychological variables implicated in driving task performance. Overall, the findings contribute to knowledge in terms of new and imaginative ways of designing future vehicle technologies in order to maximise safety, efficiency, and enjoyment.This research is funded by the Hamilton Research Studentship

Estimation of car-following safety : application to the design of intelligent cruise control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.Includes bibliographical references (leaves 101-102).by Shih-Ken Chen.Ph.D