| 1. |
- Morsali, Mahdi, et al.
(author)
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Geometrical Based Trajectory Calculation for Autonomous Vehicles in Multi-Vehicle Traffic Scenarios
- 2021
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In: 2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV). - : IEEE. - 9781728153940 ; , s. 1235-1242
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Conference paper (peer-reviewed)abstract
- A computationally cheap method for computing collision-free trajectories with multiple moving obstacles is proposed here while meeting comfort and safety criteria. By avoiding search in the trajectory calculation and instead using a geometrical set to calculate the trajectory, the calculation time is significantly reduced. The geometrical set is calculated by solving a support vector machine problem and solving the SVM problem characterizes maximum separating surfaces between obstacles and the ego vehicle in the time-space domain. The trajectory on the separating surface might not be kinematically feasible. Therefore, a vehicle model and a Newton-Raphson based procedure is proposed to obtain a safe, kinematically feasible trajectory on the separating surface. A roundabout scenario and two take-over scenarios with different configurations are used to investigate the properties of the proposed algorithm. Robustness properties of the proposed algorithm is investigated by a large number of randomly initiated simulation scenarios.
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| 2. |
- Morsali, Mahdi, 1990-, et al.
(author)
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Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines
- 2021
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In: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 6:4, s. 611-621
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Journal article (peer-reviewed)abstract
- Efficient trajectory planning of autonomous vehiclesin complex traffic scenarios is of interest both academically andin automotive industry. Time efficiency and safety are of keyimportance and here a two-step procedure is proposed. First, aconvex optimization problem is solved, formulated as a supportvector machine (SVM), in order to represent the surroundingenvironment of the ego vehicle and classify the search spaceas obstacles or obstacle free. This gives a reduced complexitysearch space and an A* algorithm is used in a state space latticein 4 dimensions including position, heading angle and velocityfor simultaneous path and velocity planning. Further, a heuristicderived from the SVM formulation is used in the A* search anda pruning technique is introduced to significantly improve searchefficiency. Solutions from the proposed planner is compared tooptimal solutions computed using optimal control techniques.Three traffic scenarios, a roundabout scenario and two complextakeover maneuvers, with multiple moving obstacles, are used toillustrate the general applicability of the proposed method.
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| 3. |
- Morsali, Mahdi, 1990-
(author)
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Trajectory Planning for an Autonomous Vehicle in Multi-Vehicle Traffic Scenarios
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Tremendous industrial and academic progress and investments have been made in au-tonomous driving, but still many aspects are unknown and require further investigation,development and testing. A key part of an autonomous driving system is an efficient plan-ning algorithm with potential to reduce accidents, or even unpleasant and stressful drivingexperience. A higher degree of automated planning also makes it possible to have a betterenergy management strategy with improved performance through analysis of surroundingenvironment of autonomous vehicles and taking action in a timely manner.This thesis deals with planning of autonomous vehicles in different urban scenarios, road,and vehicle conditions. The main concerns in designing the planning algorithms, are realtime capability, safety and comfort. The planning algorithms developed in this thesis aretested in simulation traffic situations with multiple moving vehicles as obstacles. The re-search conducted in this thesis falls mainly into two parts, the first part investigates decou-pled trajectory planning algorithms with a focus on speed planning, and the second sectionexplores different coupled planning algorithms in spatiotemporal environments where pathand speed are calculated simultaneously. Additionally, a behavioral analysis is carried outto evaluate different tactical maneuvers the autonomous vehicle can have considering theinitial states of the ego and surrounding vehicles.Particularly relevant for heavy duty vehicles, the issues addressed in designing a safe speedplanner in the first part are road conditions such as banking, friction, road curvature andvehicle characteristics. The vehicle constraints on acceleration, jerk, steering, steer ratelimitations and other safety limitations such as rollover are further considerations in speedplanning algorithms. For real time purposes, a minimum working roll model is identified us-ing roll angle and lateral acceleration data collected in a heavy duty truck. In the decoupledplanners, collision avoiding is treated using a search and optimization based planner.In an autonomous vehicle, the structure of the road network is known to the vehicle throughmapping applications. Therefore, this key property can be used in planning algorithms toincrease efficiency. The second part of the thesis, is focused on handling moving obstaclesin a spatiotemporal environment and collision-free planning in complex urban structures.Spatiotemporal planning holds the benefits of exhaustive search and has advantages com-pared to decoupled planning, but the search space in spatiotemporal planning is complex.Support vector machine is used to simplify the search problem to make it more efficient.A SVM classifies the surrounding obstacles into two categories and efficiently calculate anobstacle free region for the ego vehicle. The formulation achieved by solving SVM, con-tains information about the initial point, destination, stationary and moving obstacles.These features, combined with smoothness property of the Gaussian kernel used in SVMformulation is proven to be able to solve complex planning missions in a safe way.Here, three algorithms are developed by taking advantages of SVM formulation, a greedysearch algorithm, an A* lattice based planner and a geometrical based planner. One general property used in all three algorithms is reduced search space through using SVM. In A*lattice based planner, significant improvement in calculation time, is achieved by using theinformation from SVM formulation to calculate a heuristic for planning. Using this heuristic,the planning algorithm treats a simple driving scenario and a complex urban structureequal, as the structure of the road network is included in SVM solution. Inspired byobserving significant improvements in calculation time using SVM heuristic and combiningthe collision information from SVM surfaces and smoothness property, a geometrical planneris proposed that leads to further improvements in calculation time.Realistic driving scenarios such as roundabouts, intersections and takeover maneuvers areused, to test the performance of the proposed algorithms in simulation. Different roadconditions with large banking, low friction and high curvature, and vehicles prone to safetyissues, specially rollover, are evaluated to calculate the speed profile limits. The trajectoriesachieved by the proposed algorithms are compared to profiles calculated by optimal controlsolutions.
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| 4. |
- Shafikhani, Iman, 1988-
(author)
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Energy Management Strategy Design for Series Hybrid Electric Vehicles
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Electrification of vehicles is an indispensable step in improving fuel economy and reducing fossil fuel emissions. In particular, hybrid electric vehicle market has gained popularity as one such reliable solution. With the global rise in environmental concerns, the need for advancement of the relevant technologies has become more noticeable than before. In this pursuit, it is well-known that design of effective energy management strategies (EMS) that govern power distribution among the onboard energy sources is key in reducing fuel consumption and its adverse environmental impacts. This thesis is concerned with EMS design for series hybrid electric vehicles from two standpoints.Powertrain component durability is often neglected in EMS development. In particular, batteries are prone to degradation through usage, a phenomenon widely known as cycle aging, and contribute largely to vehicle cost. In the first part of the thesis, therefore, battery lifetime optimization is integrated into the design of fuel-efficient energy management strategies. An empirical capacity degradation model is adopted from the literature and is modified in order to predict battery lifetime. The multi-objective problem is to compromise between fuel consumption reduction and battery wear minimization. The problem is formulated within two control theory frameworks, namely Pontryagin's minimum principle and model predictive control. Simulation results suggest that there is an enormous potential in prolonging battery lifetime by sacrificing negligible to no excessive amount of fuel consumption. Performance of the developed methodology in the Pontryagin's minimum principle framework exhibits an inverse correlation with the root-mean-square of power request of drive cycles. The results can be used to develop real-time rule-based methods. The application considered in this part is a hybrid electric wheel loader. While prolonging battery lifetime is economically beneficial for any hybrid electric vehicle, the cost savings for high power applications such as the aforementioned construction equipment can be even more rewarding.The second part of the thesis is dedicated to the development of time-efficient energy management strategies. Considering the need for real-time feasibility, satisfactory fuel economy and low computation time are the key elements in EMS design. In the first step, the analytical solution to equivalent consumption minimization strategy (ECMS) for series hybrid electric vehicles is derived, where the system constraints are directly taken into account in the derivation process. The equivalence factor bounds are found and used to develop a real time adaptive ECMS. The obtained fuel economy figures are observed to be very close to the non-causal benchmarks. These results are then utilized to propose real-time predictive ECMS algorithms. Two scenarios are investigated depending on the availability of drive cycle knowledge. The first scenario corresponds to vehicles that are expected to follow certain drive cycles. This situation is common among construction machinery such as the wheel loader under study. On the other hand, there are situations where driving mission is not known in advance and the driver behavior is unpredictable, such as typical city driving. For each scenario, an algorithm is presented to compute the equivalence factor efficiently. The control action is then determined by the analytical policy derived previously. Simulations of the developed algorithms on the hybrid wheel loader and a passenger car demonstrate that the methodologies are computationally efficient and attain satisfactory fuel economy with respect to the dynamic programming benchmarks.
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| 5. |
- Shafikhani, Iman, et al.
(author)
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MPC-based energy management system design for a series HEV with battery life optimization
- 2021
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In: 2021 EUROPEAN CONTROL CONFERENCE (ECC). - : IEEE. - 9789463842365 ; , s. 2591-2596
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Conference paper (peer-reviewed)abstract
- Simultaneous optimization of fuel consumption and battery lifetime is addressed in this work. A differential capacity degradation model is used to predict capacity loss, and linear time-varying and nonlinear MPC techniques are used to solve the energy management problem. It is shown that penalizing battery power in the MPC cost function can prolong battery lifetime by about 50 percent while achieving small gains in fuel economy compared to when the cost function only aims to minimize fuel consumption. An analysis of robustness against uncertainties in drive-cycle information shows that the controller is well-behaved and has good performance under uncertainty.
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