| 1. |
- Benderius, Ola, 1985, et al.
(författare)
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A simulation environment for analysis and optimization of driver models
- 2011
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642217982 ; 6777, s. 453-462
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Konferensbidrag (refereegranskat)abstract
- A simulation environment for evaluation and optimization of driver models is introduced and described. The simulation environment features models of vehicles and drivers, as well as a representation of the traffic environment (roads, buildings etc.). In addition, an optimization framework based on stochastic optimization algorithms has been implemented as an integral part of the simulation environment. Given observed (time series) data of driver behavior and, possibly, vehicle dynamics, the optimization framework can be used for inferring driver model parameters. The simulation environment has been evaluated in two scenarios, one involving emergency braking and one involving a double lane change.
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| 2. |
- Benderius, Ola, 1985, et al.
(författare)
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Driver behaviour in unexpected critical events and in repeated exposures – a comparison
- 2014
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Ingår i: European Transport Research Review. - : Springer Science and Business Media LLC. - 1867-0717 .- 1866-8887. ; 6:1, s. 51-60
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Tidskriftsartikel (refereegranskat)abstract
- PurposeThis paper aims to determine how truck driver steering behaviour seen in repeated exposures to acritical event correlates to the behaviour resulting from an unexpected exposure to the same event.MethodsTest subjects were exposed to an unexpected critical event in a high-fidelity driving simulator. Next, a slightly modified version of the scenario was repeated several times for each subject. The driver behaviour was then analysed using standard statistical tests.ResultsIt was found that, in general, drivers keep most of their steering behaviour characteristics between test settings (unexpected and repeated). This is particularly interesting sincea similar kind of behaviour preservation is generally not found in the case of braking behaviour. In fact, onlyone significant difference was found between the two test settings, namely regarding time-to-collision at steering initiation.ConclusionsIn experiments involving both an unexpected event and several repeated events one can,at least in some cases, design the repeated event such that behavioural data collected from that setting can beused along with data from the unexpected setting. Using this procedure, one can significantly increase the amount of collected data, something that can strongly benefit, for example, driver modelling.
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| 3. |
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| 4. |
- Hoel, Carl-Johan E, 1986, et al.
(författare)
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An Evolutionary Approach to General-Purpose Automated Speed and Lane Change Behavior
- 2017
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Ingår i: Proceedings of 16th IEEE International Conference On Machine Learning And Applications (ICMLA). ; 2017-December
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a method for automatically training a general-purpose driver model, applied to the case of a truck-trailer combination. A genetic algorithm is used to optimize a structure of rules and actions, and their parameters, to achieve the desired driving behavior. The training is carried out in a simulated environment, using a two-stage process. The method is then applied to a highway driving case, where it is shown that it generates a model that matches or surpasses the performance of a commonly used reference model. Furthermore, the generality of the model is demonstrated by applying it to an overtaking situation on a rural road with oncoming traffic.
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| 5. |
- Markkula, Gustav M, 1978, et al.
(författare)
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A Review of Near-Collision Driver Behavior Models
- 2012
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Ingår i: Human Factors. - : SAGE Publications. - 1547-8181 .- 0018-7208. ; 54:6, s. 1117-1143
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Tidskriftsartikel (refereegranskat)abstract
- Objective: This article provides a review of recent models of driver behavior in on-road collision situations.Background: In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments.Method: Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios.Results: The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms.Conclusion: A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended.Application: This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work.
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| 6. |
- Markkula, Gustav M, 1978, et al.
(författare)
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Effects of experience and electronic stability control on low friction collision avoidance in a truck driving simulator
- 2013
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 50, s. 1266-1277
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Tidskriftsartikel (refereegranskat)abstract
- Two experiments were carried out in a moving-base simulator, in which truck drivers of varying experience levels encountered a rear-end collision scenario on a low-friction road surface, with and without an electronic stability control (ESC) system. In the first experiment, the drivers experienced one instance of the rear-end scenario unexpectedly, and then several instances of a version of the scenario adapted for repeated collision avoidance. In the second experiment, the unexpected rear-end scenario concluded a stretch of driving otherwise unrelated to the study presented here. Across both experiments, novice drivers were found to collide more often than experienced drivers in the unexpected scenario. This result was found to be attributable mainly to longer steering reaction times of the novice drivers, possibly caused by lower expectancy for steering avoidance. The paradigm for repeated collision avoidance was able to reproduce the type of steering avoidance situation for which critical losses of control were observed in the unexpected scenario and, here, ESC was found to reliably reduce skidding and control loss. However, it remains unclear to what extent the results regarding ESC benefits in repeated avoidance are generalisable to unexpected situations. The approach of collecting data by appending one unexpected scenario to the end of an otherwise unrelated experiment was found useful, albeit with some caveats.
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| 7. |
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| 8. |
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| 9. |
- Ström, Mikael, 1959, et al.
(författare)
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A set-based-inspired design process supported by axiomatic design and interactive evolutionary algorithms
- 2023
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Ingår i: International Journal of Product Development. - : Inderscience Publishers. - 1477-9056 .- 1741-8178. ; 27:3, s. 186-212
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an approach towards a set-based design-inspired concept development process for products with a solution space consisting principally of different solution alternatives and parameterised variants of these. The hypothesis is that such a concept development process can be based on traditional synthesis methods, an SBD-inspired elimination strategy, axiomatic design and interactive Evolutionary Algorithms (IEAs) for the synthesis of solution candidates and the successive reduction of the solution space. Axiomatic design and its axioms are used to evaluate and eliminate unfeasible alternatives, whereas IEAs, combined with human judgement, are employed for the evaluation and elimination of variants. Stated criteria specifying a design problem can be of different kinds with different ontologies described by different authors. This study focuses on functional, constraining and qualitative criteria. Results from performed industrial case studies show that the proposed method can reduce the lead time in design work.
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| 10. |
- Virgolin, Marco, 1989, et al.
(författare)
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A Mobile Interactive Robot for Social Distancing in Hospitals
- 2021
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Ingår i: Proceedings - 2021 5th IEEE International Conference on Robotic Computing, IRC 2021. ; , s. 87-91
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Konferensbidrag (refereegranskat)abstract
- We introduce the multimodal interactive mobile robot ISOLDE, intended for use in hospitals, with the primary aim of helping healthcare staff to maintain social distancing during pandemics, such as the ongoing Covid-19 pandemic. ISOLDE also addresses the growing concern related to the use of black box models in artificial intelligence, especially in situations involving high-stakes decisions. Thus, ISOLDE's interactive capabilities have been implemented using a fully interpretable dialogue manager, making it easy to monitor and, if needed, correct the robot's actions, even for a non-expert. A use case is presented (in a laboratory setting) in which the robot successfully interacts with healthcare staff to carry out a requested transportation and delivery task, and also measuring a patient's temperature.
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| 11. |
- Wahde, Mattias, 1969, et al.
(författare)
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Behavioral Selection Using the Utility Function Method: A Case Study Involving a Simple Guard Robot
- 2005
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Ingår i: 3rd International Symposium on Autonomous Minirobots for Research and Edutainment, AMiRE 2005. ; , s. 261-266
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Konferensbidrag (refereegranskat)abstract
- In this paper, the performance of the utility function method for behavioral organization is investigated in the framework of a simple guard robot. In order to achieve the best possible results, it was found that high-order polynomials should be used for the utility functions, even though the use such polynomials, involving many terms, increases the running time needed for the evolutionary algorithm to find good solutions.
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| 12. |
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| 13. |
- Wolff, Krister, 1969, et al.
(författare)
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Balancing Theory and Practical Work in a Humanoid Robotics Course
- 2010
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Ingår i: International Journal of Teaching and Learning in Higher Education (IJTLHE). - 1812-9129. ; 22:1, s. 80-88
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we summarize our experiences from teaching a course in humanoid robotics at Chalmers University of Technology in Göteborg, Sweden. We describe the robotic platform used in the course and we propose the use of a custom-built robot consisting of standard electronic andmechanical components. In our experience, by using standard components, the students obtain a deeper understanding of robotics hardware than would be possible with the use of (some) commercially available robot kits such as e.g. Boe-Bot or Lego Mindstorms. Furthermore, we propose a division between time spent on teaching the theoretical background and time spent on robot assembly and programming, which, in our view, provides the optimal balance between theory and practical work. Summarizing briefly, for a seven-week course, we propose two weeks oftheoretical background lectures, followed by five weeks of practical work, in which each practical session starts with a brief theory demonstration.
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| 14. |
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| 15. |
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| 18. |
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| 19. |
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| 20. |
- Gypa, Ioli, 1991, et al.
(författare)
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Interactive evolutionary computation for propeller design optimization of wind-assisted vessels
- 2020
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Ingår i: AIAA AVIATION 2020 FORUM. - Reston, Virginia : American Institute of Aeronautics and Astronautics. ; 1 PartF, s. 1-10
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a human-computer interaction methodology for a marine propeller design optimization problem. While the outcome of most optimization algorithms is a frontier with the best solutions, in blade design the engineers by taking into consideration all objectives and constraints want to focus on a part of this frontier and try to improve it and enhance it, in order to have more design alternatives. Thus there is a need for guiding the optimization to a specific direction, enabling the involvement of the designer in the design process. A proposal to achieving this goal is the use of interactive evolutionary computation, which is an optimization methodology based on genetic algorithms whereby the blade designer is called during the intermediate steps to visualize and assess specific areas of interest of the Pareto plot. The results have shown that the blade designer can steer the optimization to a specific direction and a more refined Pareto frontier is obtained. At the current stage there is a modest improvement of the design fitness, but a clear reduction in cost.
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| 21. |
- Gypa, Ioli, 1991, et al.
(författare)
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Propeller optimization by interactive genetic algorithms and machine learning
- 2023
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Ingår i: Ship Technology Research. - : Informa UK Limited. - 0937-7255 .- 2056-7111. ; 70:1, s. 56-71
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Tidskriftsartikel (refereegranskat)abstract
- Marine propeller design can be carried out with the aid of automated optimization, but experience shows that a such an approach has still been inferior to manual design in industrial scenarios. In this study, the automated propeller design optimization is evolved by integrating human–computer interaction as an intermediate step. An interactive optimization methodology, based on interactive genetic algorithms (IGAs), has been developed, where the blade designers systematically guide a genetic algorithm towards the objectives. The designers visualize and assess the shape of the blade cavitation and this evaluation is integrated in the optimization method. The IGA is further integrated with a support-vector machine model, in order to avoid user fatigue, IGA's main disadvantage. The results of the present study show that the IGA optimization searches solutions in a more targeted manner and eventually finds more non-dominated feasible designs that also show a good cavitation behaviour in agreement with designer preference.
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| 22. |
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| 23. |
- Hoel, Carl-Johan, 1986, et al.
(författare)
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Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
- 2020
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 5:2, s. 294-305
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Tidskriftsartikel (refereegranskat)abstract
- Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This article introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.
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| 24. |
- Hoel, Carl-Johan E, 1986, et al.
(författare)
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Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
- 2018
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. ; 2018-November, s. 2148-2155
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced. https://arxiv.org/abs/1803.10056
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| 25. |
- Hoel, Carl-Johan E, 1986, et al.
(författare)
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Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning With Applications in Autonomous Driving
- 2023
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 24:6, s. 6030-6041
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Tidskriftsartikel (refereegranskat)abstract
- Reinforcement learning (RL) can be used to create a decision-making agent for autonomous driving. However, previous approaches provide black-box solutions, which do not offer information on how confident the agent is about its decisions. An estimate of both the aleatoric and epistemic uncertainty of the agent’s decisions is fundamental for real-world applications of autonomous driving. Therefore, this paper introduces the Ensemble Quantile Networks (EQN) method, which combines distributional RL with an ensemble approach, to obtain a complete uncertainty estimate. The distribution over returns is estimated by learning its quantile function implicitly, which gives the aleatoric uncertainty, whereas an ensemble of agents is trained on bootstrapped data to provide a Bayesian estimation of the epistemic uncertainty. A criterion for classifying which decisions that have an unacceptable uncertainty is also introduced. The results show that the EQN method can balance risk and time efficiency in different occluded intersection scenarios, by considering the estimated aleatoric uncertainty. Furthermore, it is shown that the trained agent can use the epistemic uncertainty information to identify situations that the agent has not been trained for and thereby avoid making unfounded, potentially dangerous, decisions outside of the training distribution.
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| 26. |
- Hoel, Carl-Johan E, 1986, et al.
(författare)
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Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation
- 2020
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. ; , s. 1563-1569
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Konferensbidrag (refereegranskat)abstract
- Reinforcement learning (RL) can be used to create a tactical decision-making agent for autonomous driving. However, previous approaches only output decisions and do not provide information about the agent's confidence in the recommended actions. This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving. A method for classifying whether or not an action should be considered safe is also introduced. The performance of the ensemble RPF method is evaluated by training an agent on a highway driving scenario. It is shown that the trained agent can estimate the uncertainty of its decisions and indicate an unacceptable level when the agent faces a situation that is far from the training distribution. Furthermore, within the training distribution, the ensemble RPF agent outperforms a standard Deep Q-Network agent. In this study, the estimated uncertainty is used to choose safe actions in unknown situations. However, the uncertainty information could also be used to identify situations that should be added to the training process.
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| 27. |
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| 28. |
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| 30. |
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| 31. |
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| 32. |
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| 33. |
- Wolff, Krister, 1969
(författare)
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Evolutionary Humanoids for Embodied Artificial Intelligence.
- 2003
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The work presented in this thesis aims at investigating the potential of a proposed methodology to create a cognitive control architecture for a humanoid robot. This architecture comprises three hierarchical layers: the reactive layer, the model building layer, and the reasoning layer. The architecture is built on techniques from the field of evolutionary computation, and more specifically evolutionary algorithms. Based on very simple models of organic evolution, these algorithms can be applied to various problems such as combinatorial optimization problems or learning tasks. The field of artificial intelligence is discussed from a robotics viewpoint. The roles of different paradigms in AI research are considered, and so are the principles of embodiment and situatedness, which are fundamental in the behavior based robotics approach. Several evolutionary experiments performed on real, physical humanoid robot platforms are presented. These are presented mainly to motivate the use of simulated evolution for control programming of robots. In addition, these experiments constitute a subset of the necessary building blocks of the proposed cognitive humanoid robot architecture, outlined in this thesis. The experiments include sound localization, two instances of machine vision, hand-eye coordination, coordination of actuator motions in a robot foot joint, and two instances regarding learning and adaptivity.
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| 34. |
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| 35. |
- Wolff, Krister, 1969
(författare)
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Generation and Optimization of Motor Behaviors in Real and Simulated Robots
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, the problems of generating and optimizing motor behaviors for both simulated and real, physical robots have been investigated, using the paradigms of evolutionary robotics and behavior-based robotics. Specifically, three main topics have been considered: (1) On-line evolutionary optimization of hand-coded gaits for real, physical bipedal robots. The evolved gaits significantly outperformed thehand-coded gaits, reaching up to 65% higher speed. (2) Evolution of bipedal gait controllers in simulators. First, linear genetic programming was used with two different simulated bipedal robots. In both these cases, the gait controller was evolved starting from programs consisting of random sequences of basic instructions. The best evolved programs generated stable bipedal locomotion, keeping the robot upright and moving indefinitely. However, the evolved gaits were not very human-like. Thus, a different approach, inspired by the neural mechanisms involved in the locomotion of biological organisms, was tried. Here, both the structure and parameters of a central pattern generator network, controlling the locomotion of a simulated robot, were optimized using a genetic algorithm. The evolved controllers generated a stable human-like gait and were also able to handle gait transitions. (3) Behavior selection in autonomous robots, using the utility function method. In particular, the performance of the method as a function of the polynomial degree of the utility functions was investigated. It was found that adequate behavior selection systems can be found rapidly for low polynomial degrees (1-2), but also that the best solutions can only be obtained by using a higher polynomial degree (3-4). Furthermore, the performance of different evolutionary algorithms in connection with the utility function method was also investigated and, somewhat surprisingly, it was found that the standard method, employing a simple genetic algorithm, generally outperformed the modified methods.
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| 36. |
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| 37. |
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| 38. |
- Wolff, Krister, 1969, et al.
(författare)
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Walking humanoids for robotics research
- 2002
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Ingår i: Proceedings of the Second International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems (EpiRob'02), LUCS 94, Edinburgh, Scotland.. ; :LUCS 94, s. 175--176-
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Konferensbidrag (refereegranskat)
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| 39. |
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