| 1. |
- Andreotti, Eleonora, 1988, et al.
(författare)
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Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations
- 2021
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 6:2, s. 366-375
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixed-traffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic road-user behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories.
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| 2. |
- Andreotti, Eleonora, 1988, et al.
(författare)
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Potential impact of autonomous vehicles in mixed traffic from simulation using real traffic flow
- 2023
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Ingår i: Journal of Intelligent and Connected Vehicles. - 2399-9802. ; 6:1, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- This work focuses on the potential impacts of the autonomous vehicles in a mixed traffic condition represented in traffic simulator Simulation of Urban MObility (SUMO) with real traffic flow. Specifically, real traffic flow and speed data collected in 2002 and 2019 in Gothenburg were used to simulate daily flow variation in SUMO. In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles, this study focuses on mixed traffic with different percentages of autonomous and manually driven vehicles. To realize this aim, several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper. Along with the fundamental diagram, the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency. The study highlights that the autonomous vehicles' features that improve safety and efficiency in 100% autonomous and mixed traffic are different, and the ability of autonomous vehicles to switch between mixed and autonomous driving styles, and vice versa depending on the scenario, is necessary.
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| 3. |
- Andreotti, Eleonora, 1988, et al.
(författare)
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Safety-centred analysis of transition stages to traffic with fully autonomous vehicles
- 2020
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC.
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Konferensbidrag (refereegranskat)abstract
- The aim of this paper is to highlight and investigate the effects of increasing presence rate of autonomous vehicles (AVs) in terms of traffic safety and traffic flow characteristics. For this purpose, using existing driver models in traffic simulator software SUMO we identify and analyze those parameters that characterize and distinguish AVs' driving from manual driving in a heterogeneous traffic context. While it is essential to identify the parameters for traffic flow characteristics of heterogeneous fleets compared to homogeneous ones comprising manually driven vehicles (MV) only (i.e. current status), the safety aspects must be also accounted for. In order to combine these two fundamental aspects of heterogeneous traffic, we used a complete description of a highway driving scenario. The scenario integrates the perceptions of different type of vehicles (i.e. AV and MV) involved and the reaction times of human drivers and decision-making units of autonomous vehicles, to explore the impact of both the rate of AV presence and the perturbation in perception capabilities in highway scenarios.
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| 4. |
- Andreotti, Eleonora, 1988, et al.
(författare)
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Simulation-based impact projection of autonomous vehicle deployment using real traffic flow
- 2021
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- In this work we focus on future projected impacts of the autonomous vehicles in a realistic condition representing mixed traffic. By using real flow and speed data collected in 2002 and 2019 in the city of Gothenburg, we replicated and simulated the daily flow variation in SUMO. The expansion of the city in recent years was reflected in an increase in road users, and it is reasonable to expect it will increase further. Through simulations, it was possible to project this increase and to predict how this will impact the traffic in future. Furthermore, the composition of vehicle types in the future traffic can be expected to change through the introduction of autonomous vehicles. In order to predict the most likely drawbacks during the transition from a traffic consisting only manually driven vehicles to a traffic consisting only fully-autonomous vehicles, we focus on mixed traffic with different percentages of autonomous and manually driven vehicles. To realize this aim, several parameters of the car following and lane change models of autonomous vehicles are investigated in this paper. Along with the fundamental diagram, the number of lane changes and the number of conflicts are analyzed and studied as measures for improving road safety and efficiency.
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| 5. |
- Bayraktar, Ertugrul, et al.
(författare)
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Object manipulation with a variable-stiffness robotic mechanism using deep neural networks for visual semantics and load estimation
- 2020
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Ingår i: Neural Computing and Applications. - : Springer Science and Business Media LLC. - 0941-0643 .- 1433-3058. ; 32:13, s. 9029-9045
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the computer vision applications in the robotics have been improved to approach human-like visual perception and scene/context understanding. Following this aspiration, in this study, we explored the possibility of better object manipulation performance by connecting the visual recognition of objects to their physical attributes, such as weight and center of gravity (CoG). To develop and test this idea, an object manipulation platform is built comprising a robotic arm, a depth camera fixed at the top center of the workspace, embedded encoders in the robotic arm mechanism, and microcontrollers for position and force control. Since both the visual recognition and force estimation algorithms use deep learning principles, the test set-up was named as Deep-Table. The objects in the manipulation tests are selected from everyday life and are common to be seen on modern office desktops. The visual object localization and recognition processes are performed from two distinct branches by deep convolutional neural network architectures. We present five of the possible cases, having different levels of information availability on the object weight and CoG in the experiments. The results confirm that using our algorithm, the robotic arm can move different types of objects successfully varying from several grams (empty bottle) to around 250 g (ceramic cup) without failure or tipping. The proposed method also shows that connecting the object recognition with load estimation and contact point further improves the performance characterized by a smoother motion.
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| 6. |
- Boyraz Baykas, Pinar, 1981, et al.
(författare)
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Design of a Low-cost Tactile Robotic Sleeve for Autonomous Endoscopes and Catheters
- 2020
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Ingår i: Measurement and Control. - : SAGE Publications. - 0020-2940. ; 53:3-4, s. 613-626
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Tidskriftsartikel (refereegranskat)abstract
- Recent developments in medical robotics have been significant, supporting the minimally invasive operation requirements, such as smaller devices and more feedback available to surgeons. Nevertheless, the tactile feedback from a catheter or endoscopic type robotic device has been restricted mostly on the tip of the device and was not aimed to support the autonomous movement of the medical device during operation. In this work, we design a robotic sheath/sleeve with a novel and more comprehensive approach, which can function for whole-body or segment-based feedback control as well as diagnostic purposes. The robotic sleeve has several types of piezo-resistive pressure and extension sensors, which are embedded at several latitudes and depths of the silicone substrate. The sleeve takes the human skin as a biological model for its structure. It has a better tactile sensation of the inner tissues in the torturous narrow channels such as cardiovascular or endo-luminal tracts in human body thus can be used to diagnose abnormalities. In addition to this capability, using the stretch sensors distributed alongside its body, the robotic sheath/sleeve can perceive the ego-motion of the robotic backbone of the catheter and can act as a position feedback device. Because of the silicone substrate, the sleeve contributes toward safety of the medical device passively by providing a compliant interface. As an active safety measure, the robotic sheath can sense blood-clots or sudden turns inside a channel and by modifying the local trajectory, and can prevent embolisms or tissue rupture. In the future, advanced manufacturing techniques will increase the capabilities of the tactile robotic sleeve.
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| 7. |
- Boyraz Baykas, Pinar, 1981, et al.
(författare)
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Safe Human-Robot Interaction Using Variable Stiffness, Hyper-Redundancy, and Smart Robotic Skins
- 2020
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Ingår i: Service Robotics. - : IntechOpen. - 9781839680304
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In service robotics, safe human-robot interaction (HRI) is still an open research topic, requiring developments both in hardware and in software as well as their integration. In UMAY1 and MEDICARE-C2projects, we addressed both mechanism design and perception aspects of a framework for safe HRI. Our first focus was to design variable stiffness joints for the robotic neck and arm to enable inherent compliance to protect a human collaborator. We demonstrate the advantages of variable stiffness actuators (VSA) in compliancy, safety, and energy efficiency with applications in exoskeleton and rehabilitation robotics. The variable-stiffness robotic neck mechanism was later scaled down and adopted in the robotic endoscope featuring hyper-redundancy. The hyper-redundant structures are more controllable, having efficient actuation and better feedback. Lastly, a smart robotic skin is introduced to explain the safety support via enhancement of tactile perception. Although it is developed for a hyper-redundant endoscopic robotic platform, the artificial skin can also be integrated in service robotics to provide multimodal tactile feedback. This chapter gives an overview of systems and their integration to attain a safer HRI. We follow a holistic approach for inherent compliancy via mechanism design (i.e., variable stiffness), precise control (i.e., hyper-redundancy), and multimodal tactile perception (i.e., smart robotic-skins).
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| 8. |
- Boyraz Baykas, Pinar, 1981
(författare)
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Semantic Analysis of Driver Behavior by Data Fusion
- 2020
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Ingår i: Vehicles, Drivers, and Safety, Edited by: Huseyin Abut, Kazuya Takeda, Gerhard Schmidt and John H.L. Hansen, (Intelligent Vehicles and Transportation, 2). - 9783110666472
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Behavioral signal processing and data-fusion have been two important components of the analytical toolbox that is used to understand driver behavior and implement advanced driver assistance systems (ADAS). The recent need for quantitative analysis of driver behavior is now driven by a new revelation that incorporating human-like behavior and control strategies in the autonomous vehicles can increase their safety and acceptability in a mixed-fleet traffic environment. In addition to that, the overall safety and efficiency of the driver-vehicle system in a conditional or partial automation (Level 2–4) can be leveraged if the perception, cognition, and action capabilities of driver are enhanced based on driving-task or traffic-scenario. Motivated by this new interest, this work attempts to define a highlevel semantic analysis framework incorporating eye-motion, road-scene, and vehicle dynamics data. The study aims to identify general trends or patterns in driver behavior, especially concerning focus of attention (FoA), based on two categories: traffic scenario and complexity. To perform semantic analysis, open database from DR(eye)VE Project is used. First, the road-scene video and vehicle dynamics data are used together to obtain a complexity measure in addition to automatic recognition of the traffic-scenario. Next, the raw eye-movement data is processed to obtain gaze distribution maps and metrics. Then, a support vector machine (SVM) is trained using gaze metrics to infer the complexity level or the traffic-scenario. To obtain better separation between two classes (i.e., low vs high complexity or urban vs highway scenarios), the SVM is trained using Bayesian optimization. The results showed that based on the gaze distribution, it is possible to distinguish between urban and highway scenarios (85% accuracy), while this distinction between complexity levels can be even stronger (98% accuracy). The framework can be used as a high-level analysis and inference tool to discover behavioral characteristics of drivers and their relation to FoA patterns.
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| 9. |
- Pereira, Mike, 1992, et al.
(författare)
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Parameter and density estimation from real-world traffic data: A kinetic compartmental approach
- 2022
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Ingår i: Transportation Research Part B: Methodological. - : Elsevier BV. - 0191-2615. ; 155, s. 210-239
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Tidskriftsartikel (refereegranskat)abstract
- The main motivation of this work is to assess the validity of a LWR traffic flow model to model measurements obtained from trajectory data, and propose extensions of this model to improve it. A formulation for a discrete dynamical system is proposed aiming at reproducing the evolution in time of the density of vehicles along a road, as observed in the measurements. This system is formulated as a chemical reaction network where road cells are interpreted as compartments, the transfer of vehicles from one cell to the other is seen as a chemical reaction between adjacent compartment and the density of vehicles is seen as a concentration of reactant. Several degrees of flexibility on the parameters of this system, which basically consist of the reaction rates between the compartments, can be considered: a constant value or a function depending on time and/or space. Density measurements coming from trajectory data are then interpreted as observations of the states of this system at consecutive times. Optimal reaction rates for the system are then obtained by minimizing the discrepancy between the output of the system and the state measurements. This approach was tested both on simulated and real data, proved successful in recreating the complexity of traffic flows despite the assumptions on the flux-density relation.
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| 10. |
- Yigit, Cihat Bora, et al.
(författare)
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External Force/Torque Estimation With Only Position Sensors for Antagonistic VSAs
- 2021
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Ingår i: IEEE Transactions on Robotics. - 1552-3098 .- 1941-0468. ; 37:2, s. 675-682
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Tidskriftsartikel (refereegranskat)abstract
- Recent use scenarios involving human-robot collaboration have revealed that the robots require elastic joints to safely interact with humans. It is also critical to know applied force/torque (f/t) during the interaction for control and motion planning purposes. In this article, we estimate the external f/t values without using any sensors other than low-cost encoders by exploiting the inherent elastic properties of the joint. For estimation, the following two different approaches are used: model based and model free. In the model-based approach, an extended Kalman filter (EKF) and an external force observer (EFOB) are used considering the dynamical behavior of the system to estimate the interaction force. In the model-free approach, the artificial neural network (ANN) utilizes the data gathered from mechanical systems. In comparative analysis, we have, therefore, considered three different estimation methods, two of which are model based and the remaining one is model free (i.e., data driven). Implementing these estimation algorithms experimentally on a variable stiffness joint, we performed an extensive evaluation of their performances. All methods show similar level of performance in terms of the root-mean-square (RMS) error with 0.0847, 0.0841, and 0.1082 N for the EKF, EFOB, and ANN, respectively. Model-based methods do not require continuous data stream through the experimental set up. On the other hand, the ANN does not need an explicit model of the system; therefore, it may become preferable when the detailed model derivation is not possible.
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