1,720,964 research outputs found
Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving with Road Users
Get PDFFor motion planning and control of autonomous vehicles to be proactive and safe, pedestrians\u27 and other road users\u27 motions must be considered. In this paper, we present a vehicle motion planning and control framework, based on Model Predictive Control, accounting for moving obstacles. Measured pedestrian states are fed into a prediction layer which translates each pedestrians\u27 predicted motion into constraints for the MPC problem.Simulations and experimental validation were performed with simulated crossing pedestrians to show the performance of the framework. Experimental results show that the controller is stable even under significant input delays, while still maintaining very low computational times. In addition, real pedestrian data was used to further validate the developed framework in simulations
A Computationally Efficient Model for Pedestrian Motion Prediction
Get PDFWe present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving. The model is based on a road map structure, and assumes a rational pedestrian behavior. We compare our model with the state-of-the art and discuss its accuracy, and limitations, both in simulations and in comparison to real data
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Optimization of driver model parameters for Long Combination Vehicles
No full textLong combination vehicles (LCVs) are modular combination vehicles that are longer and heavier than what currently is allowed on European roads. These vehicle combinations have the potential to cut down overall transportation costs, but also carbon dioxide emissions. Countries such as Canada and Australia already have these truck combinations driving on their roads, and their use on European roads is expected to increase in the near future. The LCVs however bring an undesired effect of increased difficulty of maneuvering on roads and in traffic. Thus, their increased complexity calls for driver assisting systems. The development of these systems leads to promising ways of improving traffic flow and increase the use of long combination trucks on current roads. In this thesis an existing framework for automated driving has been used which utilizes driver models for the navigation of the LCV. The trajectories of the LCV are generated using numerical simulations of non-linear ordinary differential equations (ODEs). The actuation requests, which are front wheel steering, propulsion and braking are calculated using driver models. Up until now the parameters of the driver models have been fixed, and were set by fitting data after an on-road study with professional truck drivers. An approach for optimization of driver model parameters has been proposed in this thesis, which involves genetic algorithms (GAs) and particle swarm optimization (PSO). In order to achieve a real-time feasible implementation, the highly parallel nature of the GA and PSO are utilized. OpenCL was used as a platform to implement the parallel processes for both algorithms which allowed for code execution on either CPU or GPU. Optimzation of the driver model parameters showed that it could for a given dangerous scenario successfully abort or complete a driving maneuver within given safety limits. The use of stochastic optimization proved to be reliable and solutions were often found 100% of the time. As for the real time aspect of the optimization, the results hinted that by lowering the number of iteration steps, optimizing code and upgrading the used hardware, a real time implementation is within reach
Optimization of driver model parameters for Long Combination Vehicles
No full textLong combination vehicles (LCVs) are modular combination vehicles that are longer and heavier than what
currently is allowed on European roads. These vehicle combinations have the potential to cut down overall
transportation costs, but also carbon dioxide emissions. Countries such as Canada and Australia already have
these truck combinations driving on their roads, and their use on European roads is expected to increase in the
near future. The LCVs however bring an undesired effect of increased difficulty of maneuvering on roads and in
traffic. Thus, their increased complexity calls for driver assisting systems. The development of these systems
leads to promising ways of improving traffic flow and increase the use of long combination trucks on current
roads.
In this thesis an existing framework for automated driving has been used which utilizes driver models for the
navigation of the LCV. The trajectories of the LCV are generated using numerical simulations of non-linear
ordinary differential equations (ODEs). The actuation requests, which are front wheel steering, propulsion and
braking are calculated using driver models. Up until now the parameters of the driver models have been fixed,
and were set by fitting data after an on-road study with professional truck drivers.
An approach for optimization of driver model parameters has been proposed in this thesis, which involves
genetic algorithms (GAs) and particle swarm optimization (PSO). In order to achieve a real-time feasible
implementation, the highly parallel nature of the GA and PSO are utilized. OpenCL was used as a platform to
implement the parallel processes for both algorithms which allowed for code execution on either CPU or GPU.
Optimzation of the driver model parameters showed that it could for a given dangerous scenario successfully
abort or complete a driving maneuver within given safety limits. The use of stochastic optimization proved to
be reliable and solutions were often found 100% of the time. As for the real time aspect of the optimization,
the results hinted that by lowering the number of iteration steps, optimizing code and upgrading the used
hardware, a real time implementation is within reach
Enabling Safe Autonomous Driving in Uncertain Environments [Elektronisk resurs]
No full textAutonomous driving technologies have been developed in the past decades with the objective of increasing safety and efficiency. However, in order to enable such systems to be deployed on a global scale, the problems and concerns regarding safety must be addressed. The difficulty in providing safety guarantees for autonomous driving applications comes from the fact that the self-driving vehicle needs to be able to handle a diverse set of environments and traffic situations. More specifically, it must be able to interact with other road users, whose intentions cannot be perfectly known. This thesis proposes a Model Predictive Control (MPC) approach to ensure safe autonomous driving in uncertain environments. While MPC has been widely used in motion planning and control for autonomous driving applications, the standard literature cannot be directly applied to ensure safety (recursive feasibility) in the presence of other road users, i.e., pedestrians, cyclists, and other vehicles. To that end, this thesis shows how recursive feasibility can still be obtained through a slight modification of the MPC controller design. The results of this thesis build upon the assumption that the behavior of the surrounding environment can be predicted to some extent, i.e., a future motion trajectory with some uncertainty bound can be propagated. Then, by postulating the existence of a safe set for the autonomous driving problem, and requiring that the motion prediction models have a consistent structure, safety guarantees can be derived for an MPC controller. Finally, this thesis shows that the proposed MPC framework does not only hold in theory and simulations, but that it can also be deployed on a real vehicle test platform and operate in real-time, while still ensuring that the conditions needed for the derived safety guarantees hold
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Enabling Safe Autonomous Driving in Uncertain Environments
Get PDFAutonomous driving technologies have been developed in the past decades with the objective of increasing safety and efficiency. However, in order to enable such systems to be deployed on a global scale, the problems and concerns regarding safety must be addressed. The difficulty in providing safety guarantees for autonomous driving applications comes from the fact that the self-driving vehicle needs to be able to handle a diverse set of environments and traffic situations. More specifically, it must be able to interact with other road users, whose intentions cannot be perfectly known.This thesis proposes a Model Predictive Control (MPC) approach to ensure safe autonomous driving in uncertain environments. While MPC has been widely used in motion planning and control for autonomous driving applications, the standard literature cannot be directly applied to ensure safety (recursive feasibility) in the presence of other road users, i.e., pedestrians, cyclists, and other vehicles. To that end, this thesis shows how recursive feasibility can still be obtained through a slight modification of the MPC controller design.The results of this thesis build upon the assumption that the behavior of the surrounding environment can be predicted to some extent, i.e., a future motion trajectory with some uncertainty bound can be propagated. Then, by postulating the existence of a safe set for the autonomous driving problem, and requiring that the motion prediction models have a consistent structure, safety guarantees can be derived for an MPC controller.Finally, this thesis shows that the proposed MPC framework does not only hold in theory and simulations, but that it can also be deployed on a real vehicle test platform and operate in real-time, while still ensuring that the conditions needed for the derived safety guarantees hold
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