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  • 1.
    Morsali, Mahdi
    et al.
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Frisk, Erik
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Åslund, Jan
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Deterministic Trajectory Planning for Non-Holonomic Vehicles Including Road Conditions, Safety and Comfort Factors2019Ingår i: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, nr 5, s. 97-102Konferensbidrag (Refereegranskat)
    Abstract [en]

    Deterministic and real time calculation of safe and comfortable speed profiles is the main topic of this paper. Using vehicle properties and road characteristics, such as friction and road banking, safety limits for rollover and skidding are calculated and applied in the trajectory planning. To satisfy comfort criteria and obtain smooth speed profiles, jerk and acceleration of the vehicle are limited in the speed planning algorithm. For speed planner, an A* based search method is used to calculate a speed profile corresponding to shortest traveling time. In order to avoid stationary and moving obstacles, decoupled prioritized planning is used. A physical model is used to define the behavior of the vehicle in the speed planner, where jerk is main parameter for speed planner. The physical model enables the algorithm to take into account the safety and comfort limitations. The results attained from the search method are compared with optimal solutions in different test scenarios and the comparisons show the properties of the algorithm. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

  • 2.
    Morsali, Mahdi
    et al.
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Frisk, Erik
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Åslund, Jan
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Real-time velocity planning for heavy duty truck with obstacle avoidance2017Ingår i: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017), IEEE , 2017, s. 109-114Konferensbidrag (Refereegranskat)
    Abstract [en]

    A model predictive controller (MPC) including velocity and path planner is designed for real time calculation of a safe and comfortable velocity and steer angle in a heavy duty vehicle. The calculation time is reduced by finding, based on measurement data, simple roll and motion model. The roll dynamics of the truck is constructed using identification of proposed roll model and it is validated by measurements logged by a heavy duty truck and the suggested model shows good agreement with the measurement data. The safety issues such as rollover prevention and moving obstacle avoidance are taken into account. To increase comfort, acceleration, jerk, steer angle and steer angle rate are limited. The simulation and control algorithm is tested in different scenarios, where the test results show the properties of the algorithm.

  • 3.
    Morsali, Mahdi
    et al.
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Mohseni, Fatemeh
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Frisk, Erik
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Deterministic Path Planning for Non-Holonomic Vehicles Including Friction and Steer Rate Limitations2017Ingår i: 2017 IEEE INTERNATIONAL CONFERENCE ON VEHICULAR ELECTRONICS AND SAFETY (ICVES), IEEE , 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Path planning algorithms have evolved during decades to become computationally less expensive and optimal. In this paper a deterministic approach is used to find a path near to the shortest path using motion primitives. The motion primitives are constructed using a non-holonomic vehicle model. The physical model enables the algorithm to use a friction map and calculate paths with lower lateral slip forces. Furthermore the algorithm takes into account the steer rate using steer angles assigned for motion primitives. The algorithm is an A* based search method along with a heuristic to find a near optimal solution. The performance and calculation time of the algorithm is tunable by adjusting motion primitive size and discretization steps. In order to compare the algorithm output to optimal solution a direct multiple shooting method is used. The algorithm is simulated in different scenarios that shows the properties of the algorithm. The results attained from search method is compared with optimal solution in two different test scenarios and the comparison shows consistency of search method to the optimal solution.

  • 4.
    Morsali, Mahdi
    et al.
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Åslund, Jan
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Frisk, Erik
    Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
    Trajectory Planning in Traffic Scenarios Using Support Vector Machines2019Ingår i: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, nr 5, s. 91-96Konferensbidrag (Refereegranskat)
    Abstract [en]

    Finding safe and collision free trajectories in an environment with moving obstacles is central for autonomous vehicles but at the same time a complex task. A reason is that the search space in space-time domain is very complex. This paper proposes a two-step approach where in first step, the search space for trajectory planning is simplified by solving a convex optimization problem formulated as a Support Vector Machine resulting in an obstacle free corridor that is suitable for a trajectory planner. Then, in a second step, a basic A* search strategy is used in the obstacle free search space. Due to the physical model used, the comfort and safety criteria are applied while searching the trajectory. The vehicle rollover prevention is used as a safety criterion and the acceleration, jerk and steering angle limits are used as comfort criteria. For simulations, urban environments with intersections and vehicles as moving obstacles are constructed. The properties of the approach are examined by the simulation results. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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