| 1. |
- Anistratov, Pavel, 1990-
(författare)
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Autonomous Avoidance Maneuvers for Vehicles using Optimization
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To allow future autonomous passenger vehicles to be used in the same driving situations and conditions as ordinary vehicles are used by human drivers today, the control systems must be able to perform automated emergency maneuvers. In such maneuvers, vehicle dynamics, tire–road interaction, and limits on what the vehicle is capable of performing are key factors to consider. After detecting a static or moving obstacle, an avoidance maneuver or a sequence of lane changes are common ways to mitigate the critical situation. For that purpose, motion planning is important and is a primary task for autonomous-vehicle control subsystems. Optimization-based methods and algorithms for such control subsystems are the main focus of this thesis.Vehicle-dynamics models and road obstacles are included as constraints to be fulfilled in an optimization problem when finding an optimal control input, while the available freedom in actuation is utilized by defining the optimization criterion. For the criterion design, a new proposal is to use a lane-deviation penalty, which is shown to result in well-behaved maneuvers and, in comparison to minimum-time and other lateral-penalty objective functions, decreases the time that the vehicle spends in the opposite lane.It is observed that the final phase of a double lane-change maneuver, also called the recovery phase, benefits from a dedicated treatment. This is done in several steps with different criteria depending on the phase of the maneuver. A theoretical redundancy analysis of wheel-torque distribution, which is derived independently of the optimization criterion, complements and motivates the suggested approach.With a view that a complete maneuver is a sequence of two or more sub-maneuvers, a decomposition approach resulting in maneuver segments is proposed. The maneuver segments are shown to be possible to determine with coordinated parallel computations with close to optimal results. Suitable initialization of segmented optimizations benefits the solution process, and different initialization approaches are investigated. One approach is built upon combining dynamically feasible motion candidates, where vehicle and tire forces are important to consider. Such candidates allow addressing more complicated situations and are computed under dynamic constraints in the presence of body and wheel slip. To allow a quick reaction of the vehicle control system to moving obstacles and other sudden changes in the conditions, a feedback controller capable of replanning in a receding-horizon fashion is developed. It employs a coupling between motion planning using a friction-limited particle model and a novel low-level controller following the acceleration-vector reference of the computed plan. The controller is shown to have real-time performance.
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| 2. |
- Liang, Kuo-Yun
(författare)
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Fuel-Efficient Heavy-Duty Vehicle Platoon Formation
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There is a need for intelligent freight transport solutions as the demand for road freight transport is continuously increasing while carbon footprint needs to be significantly reduced. Heavy-duty vehicle (HDV) platooning is one potential solution to partially mitigate the environmental impacts as well as to reduce fuel consumption, improve traffic safety, and increase traffic throughput on congested highways. However, as each goods transport has different origin, destination, and time restriction, it is not evident how the HDVs, carrying the goods, can fully utilize the platooning benefits during individual transport missions. Thus, there is a need to systematically coordinate scattered vehicles on the road to form platoons in order to maximize the benefits of platooning. This thesis addresses the problem of merging scattered HDVs to form platoons in traffic. The first contribution of the thesis is the investigation of how and when a pair of HDVs should form platoons given their positions, speeds, and destinations. We formulate the problem as an optimization problem and we derive a break-even ratio that describes how far a vehicle should check for possible vehicles to platoon with. The second contribution is to consider traffic during the merging maneuver when forming a platoon. Traffic may disturb and delay when the two HDVs will form a platoon and such delay leads to less fuel saved than initially planned. Based on shockwave and moving bottleneck theories, we derive a merge distance predictor that calculates where the HDVs will merge depending on the traffic condition. We first validate this in a microscopic traffic simulation tool. Then, we also conduct an experimental study during one month on a public highway between Stockholm and Södertälje to evaluate the merging maneuver with different traffic densities. Lastly, we use vehicle probe data obtained from a fleet management system to investigate the potential fuel savings from coordination in a larger road network. The number of vehicles platooning can be increased significantly through coordination compared to today. The main result of this thesis indicates that merging HDVs to form platoons leads to great fuel savings and that there are significant potentials to do so in reality. Traffic needs to be considered in order to guarantee that the HDVs save fuel and deliver the goods in time. Furthermore, the earlier the transport assignment is planned ahead of time, the more opportunities there are to collaborate with other fleet owners to reduce the fuel consumption.
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| 3. |
- Lima, Pedro, 1990-
(författare)
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Predictive control for autonomous driving : With experimental evaluation on a heavy-duty construction truck
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous vehicles is a rapidly expanding field, and promise to play an important role in society. In more isolated environments, vehicle automation can bring significant efficiency and production benefits and it eliminates repetitive jobs that can lead to inattention and accidents.The thesis addresses the problem of lateral and longitudinal dynamics control of autonomous ground vehicles with the purpose of accurate and smooth path following. Clothoids are used in the design of optimal predictive controllers aimed at minimizing the lateral forces and jerks in the vehicle.First, a clothoid-based path sparsification algorithm is proposed to efficiently describe the reference path. This approach relies on a sparseness regularization technique such that a minimal number of clothoids is used to describe the reference path.Second, a clothoid-based model predictive controller (MPCC) is proposed. This controller aims at producing a smooth driving by taking advantage of the clothoid properties. Third, we formulate the problem as an economic model predictive controller (EMPC). In EMPC the objective function contains an economic cost (here represented by comfort or smoothness), which is described by the second and first derivatives of the curvature. Fourth, the generation of feasible speed profiles, and the longitudinal vehicle control for following these, is studied. The speed profile generation is formulated as an optimization problem with two contradictory objectives: to drive as fast as possible while accelerating as little as possible. The longitudinal controller is formulated in a similar way, but in a receding horizon fashion.The experimental evaluation with the EMPC demonstrates its good performance, since the deviation from the path never exceeds 30 cm and in average is 6 cm. In simulation, the EMPC and the MPCC are compared with a pure-pursuit controller (PPC) and a standard MPC. The EMPC clearly outperforms the PPC in terms of path accuracy and the standard MPC in terms of driving smoothness.
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| 4. |
- Mohseni, Fatemeh, 1984-
(författare)
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Decentralized Optimal Control for Multiple Autonomous Vehicles in Traffic Scenarios
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- New transport technologies have the potential to create more efficient modes of transport and transforming cities for the better by improving urban productivity and increasing efficiency of its transport system to move consumers, labor, and freight. Traffic accidents, energy consumption, pollution, congestion, and long commuting times are main concerns and new transport technologies with autonomous vehicles have the potential to be part of the solution to these important challenges. An autonomous, or highly automated car is a vehicle that can operate with little to no human assistance. This technology is not yet generally available, but if fully realized have the potential to fundamentally change the transportation system. The passenger experience will fundamentally change, but there are also possibilities to increase traffic flow, form platoons of transport vehicles to reduce air-drag and thereby energy consumption, and a main challenge is to realize all this in a safe way in uncertain and complex traffic situations on highways and in urban scenarios. The key topic of this dissertation is how optimal control techniques, more specifically Model Predictive Control (MPC), can be applied in autonomous driving in dynamic environments and with dynamic constraints on vehicle behavior. The main problem studied is how to control multiple vehicles in an optimal, safe, and collision free way in complex traffic scenarios, e.g., laneswitching, merging, or intersection situations in the presence of moving obstacles, i.e., other vehicles whose behavior and intent may not be known. Further, the controller needs to take maneuvering capabilities of the vehicle into account, respecting road boundaries, speed limitations, and other traffic rules. Optimization-based techniques for control are interesting candidates for multi-vehicle problems, respecting well-defined rules in traffic while still providing a high degree of decision autonomy to each vehicle. To ensure autonomy, it is studied how to decentralize the control approach to not rely on a centralized computational resource. Different methods and approaches are proposed in the thesis with guaranteed convergence and collision-avoidance features. To reduce the computational complexity of the controller, a Gaussian risk model for collision prediction is integrated and also a technique that combines MPC with learning methods is explored. Main contributions of this dissertation are control methods for autonomous vehicles that provide safety and comfort of passengers even in uncertain traffic situations where the behavior of surrounding vehicles is uncertain, and the methods are computationally fast enough to be used in real time. An important property is that the proposed algorithms are general enough to be used in different traffic scenarios, hence reducing the need for specific solutions for specific situations.
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| 5. |
- Ljungqvist, Oskar, 1990-
(författare)
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On motion planning and control for truck and trailer systems
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. Thanks to this technology enhancement, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems (ADAS) and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed areas, such as mines, harbors and loading/offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, different truck and trailer systems are used to transport materials. These systems are composed of several interconnected modules, and are thus large and highly unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control frameworks for such systems.First, a cascade controller for a reversing truck with a dolly-steered trailer is presented. The unstable modes of the system is stabilized around circular equilibrium configurations using a gain-scheduled linear quadratic (LQ) controller together with a higher-level pure pursuit controller to enable path following of piecewise linear reference paths. The cascade controller is then used within a rapidly-exploring random tree (RRT) framework and the complete motion planning and control framework is demonstrated on a small-scale test vehicle.Second, a path following controller for a reversing truck with a dolly-steered trailer is proposed for the case when the obtained motion plan is kinematically feasible. The control errors of the system are modeled in terms of their deviation from the nominal path and a stabilizing LQ controller with feedforward action is designed based on the linearization of the control error model. Stability of the closed-loop system is proven by combining global optimization, theory from linear differential inclusions and linear matrix inequality techniques.Third, a systematic framework is presented for analyzing stability of the closed-loop system consisting of a controlled vehicle and a feedback controller, executing a motion plan computed by a lattice planner. When this motion planner is considered, it is shown that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, a novel method is presented for analyzing the behavior of the tracking error, how to design the feedback controller and how to potentially impose constraints on the motion planner in order to guarantee that the tracking error is bounded and decays towards zero.Fourth, a complete motion planning and control solution for a truck with a dolly-steered trailer is presented. A lattice-based motion planner is proposed, where a novel parametrization of the vehicle’s state-space is proposed to improve online planning time. A time-symmetry result is established that enhance the numerical stability of the numerical optimal control solver used for generating the motion primitives. Moreover, a nonlinear observer for state estimation is developed which only utilizes information from sensors that are mounted on the truck, making the system independent of additional trailer sensors. The proposed framework is implemented on a full-scale truck with a dolly-steered trailer and results from a series of field experiments are presented.
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