On Curve Negotiation: From Driver Support to Automation
19 Feb 2015-IEEE Transactions on Intelligent Transportation Systems (IEEE)-Vol. 16, Iss: 4, pp 2082-2093
TL;DR: A curve negotiation “behavior” that can be used-within subsumption architectures - to produce artificial agents with the ability to negotiate curves in a humanlike way may be used to implement functions spanning different levels of automation.
Abstract: This paper describes a curve negotiation “behavior” that can be used—within subsumption architectures—to produce artificial agents with the ability to negotiate curves in a humanlike way. This may be used to implement functions spanning different levels of automation, from assistance (curve warning) to automated (curve speed control). This paper gives the following: 1) a summary of related works and of the subsumption architecture conceptual framework; 2) a detailed description of the function within this framework; 3) experimental data for validation and tuning derived from user tests; 4) guidelines on integration of the function within advanced driver assistance systems with different automation levels, with examples; and 5) a comparison with experimental data of the human curve speed choice models in the state of the art.
Citations
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TL;DR: A novel yet efficient deep learning method for analyzing the driver behavior by learning a 2D Convolutional Neural Network on images constructed from driving signals based on recurrence plot technique.
Abstract: Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring the driver behavior are rely on computer vision techniques. Such methods suffer from violation of privacy and the possibility of spoofing. This paper presents a novel yet efficient deep learning method for analyzing the driver behavior. We have used the driving signals, including acceleration, gravity, throttle, speed, and Revolutions Per Minute (RPM) to recognize five types of driving styles, including normal, aggressive, distracted, drowsy, and drunk driving. To take the advantages of successful deep neural networks on images, we learn a 2D Convolutional Neural Network (CNN) on images constructed from driving signals based on recurrence plot technique. Experimental results confirm that the proposed method can efficiently detect the driver behavior.
142 citations
TL;DR: An overview of the current state of the art in the key aspects of autonomous driving is provided, based on the information received in situ from top research centers in the field and on a literature review, to discuss different approaches regarding autonomous traffic and propose a framework for future research.
Abstract: Autonomous driving is expected to revolutionize road traffic attenuating current externalities, especially accidents and congestion. Carmakers, researchers and administrations have been working on autonomous driving for years and significant progress has been made. However, the doubts and challenges to overcome are still huge, as the implementation of an autonomous driving environment encompasses not only complex automotive technology, but also human behavior, ethics, traffic management strategies, policies, liability, etc. As a result, carmakers do not expect to commercially launch fully driverless vehicles in the short-term. From the technical perspective, the unequivocal detection of obstacles at high speeds and long distances is one of the greatest difficulties to face. Regarding traffic management strategies, all approaches share the vision that vehicles should behave cooperatively. General V2V cooperation and platooning are options being discussed, both with multiple variants. Various strategies, built from different standpoints, are being designed and validated using simulation. Besides, legal issues have already been arisen in the context of highly-automated driving. They range from the need for special driving licenses to much more intricate topics like liability in the event of an accident or privacy issues. All these legal and ethical concerns could hinder the spread of autonomous vehicles once technologically feasible. This paper provides an overview of the current state of the art in the key aspects of autonomous driving. Based on the information received in situ from top research centers in the field and on a literature review, authors highlight the most important advances and findings reached so far, discuss different approaches regarding autonomous traffic and propose a framework for future research.
133 citations
TL;DR: In this article, the authors proposed a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given, and the overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a realtime vehicle dynamics simulation; the performance was then compared with a typical planning approach.
Abstract: The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.
94 citations
Cites background or methods from "On Curve Negotiation: From Driver S..."
...However, because the dynamic motion of the vehicle is not considered to be the whole trajectory when only the above equation is used, it is difficult to maintain the balance and stability of the vehicle in cornering, owing to the curve negotiation problem [29]....
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...o ever, because the dyna ic otion of the vehicle is not considered to be the hole trajectory hen only the above equation is used, it is difficult to aintain the balance and stability of the vehicle in cornering, o ing to the curve negotiation proble [29]....
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61 citations
TL;DR: A motion planner tailored for particular requirements for robotic car navigation is presented, which combines competent exploratory nature of the randomized search methods with vector-valued parameterization steering, and which outperforms recently proposed planners by using an efficient bidirectional RRT-based search.
Abstract: This paper presents a motion planner tailored for particular requirements for robotic car navigation. We leverage B-spline curve properties to include vehicle's constraint requirements, thus lowering the search dimensionality. An algorithm, which combines competent exploratory nature of the randomized search methods with vector-valued parameterization steering, is developed here. Vehicle's limitations, along with obstacle's constraints, are satisfied without being hindered by numerical integration and control space discretization of traditional randomized kinodynamic planners. We rely on newly developed theoretical underpinnings to overcome performance issues in rapidly exploring random tree (RRT) solutions. Rigorous simulations and analysis demonstrate that this new approach outperforms recently proposed planners by using an efficient bidirectional RRT-based search, by maintaining continuous state and control spaces, and generating C2 continuous paths, which are realistic inputs suited for mobile robotic applications and passenger vehicles.
58 citations
References
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Book•
01 Jun 1991
TL;DR: A new architecture for controlling mobile robots is described, building a robust and flexible robot control system that has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms.
Abstract: A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
7,759 citations
"On Curve Negotiation: From Driver S..." refers background in this paper
...The idea of structuring complex behaviors by means of layered control architectures dates back to mid 1980s [1]....
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...• Scalability: new tasks and new goals can be added in an easier way, potentially achieving more and more complex functions [1]....
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01 Mar 1986
TL;DR: In this paper, a new architecture for controlling mobile robots is described, which is made up of asynchronous modules that communicate over low-bandwidth channels, each module is an instance of a fairly simple computational machine.
Abstract: A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
7,291 citations
TL;DR: This theory provides a simple and powerful unifying perspective for both eye and arm movement control and accurately predicts the trajectories of both saccades and arm movements and the speed–accuracy trade-off described by Fitt's law.
Abstract: When we make saccadic eye movements or goal-directed arm movements, there is an infinite number of possible trajectories that the eye or arm could take to reach the target1,2. However, humans show highly stereotyped trajectories in which velocity profiles of both the eye and hand are smooth and symmetric for brief movements3,4. Here we present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal. We propose that in the presence of such signal-dependent noise, the shape of a trajectory is selected to minimize the variance of the final eye or arm position. This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed–accuracy trade-off described by Fitt's law5. These profiles are robust to changes in the dynamics of the eye or arm, as found empirically6,7. Moreover, the relation between path curvature and hand velocity during drawing movements reproduces the empirical ‘two-thirds power law’8,9. This theory provides a simple and powerful unifying perspective for both eye and arm movement control.
2,348 citations
"On Curve Negotiation: From Driver S..." refers background or methods in this paper
...In fact, inverse correlation between curvature and speed has been explained as a strategy for minimizing the effects of steering errors [16], [17], or for achieving robust control in the more general domain of human sensory-motor strategies [22] (see [7] for further discussion)....
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...To support the sensory-motor interpretation for the speed choice, it must be mentioned that similar strategies have been discovered for hand-tracing [20] and for walking [21] and explained in terms of the minimum variance principle [22]....
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...approximates robust control (see also [22] and Section I-B)....
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TL;DR: This work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online, allowing researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function.
Abstract: The sensorimotor system is a product of evolution, development, learning and adaptation-which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.
1,650 citations
"On Curve Negotiation: From Driver S..." refers background in this paper
...a convenient mathematical tool for such problem category, but mainly because of the consolidated opinion—in cognitive and behavioral sciences—that optimal control successfully models flexible/optimized human sensory-motor strategies [29]–[31]....
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...10 shows the artificial curve behavior generated with the above criterion using OFC (i.e., continuous update of the target plan)....
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...It has to be noted that this scheme has some similarity with human optimal feedback control (OFC) [29], [40], with which it shares desirable features, such as the fact that the update rate may be slower than the tracking rate, and that only task-relevant deviations occurring between updates are corrected (minimum intervention principle) [30], [41]....
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...It has to be noted that this scheme has some similarity with human optimal feedback control (OFC) [29], [40], with which it shares desirable features, such as the fact that the update rate...
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TL;DR: It is proposed that the vertebrate basal ganglia have evolved as a centralized selection device, specialized to resolve conflicts over access to limited motor and cognitive resources.
1,182 citations
"On Curve Negotiation: From Driver S..." refers background in this paper
...However, for more complex tasks, action selection may be more complex [37]....
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...This idea conforms to recent theories in cognitive behavioral sciences, such as the theory of affordance competition [36], which posits that multiple possible actions are produced simultaneously at the cortical level, and only one is selected at the basal ganglia [37]....
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