| 1. |
- Parseh, Masoumeh, 1989-, et al.
(author)
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Pre-Crash Vehicle Control and Manoeuvre Planning: A Step Towards Minimizing Collision Severity for Highly Automated Vehicles
- 2019
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In: 2019 IEEE International Conference of Vehicular Electronics and Safety (ICVES). - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- This paper addresses the control of a highly automated vehicle in a traffic scenario, where colliding with other traffic agents is unavoidable. Such a critical situation could be the result of a fault in the vehicle, late obstacle detection or the presence of an aggressive driver. We provide an approach that allows the vehicle’s control system to choose the manoeuvre that is likely to lead to the least severe injuries to vehicle occupants.The approach involves the off-line solving of an optimal control problem to create a set of trajectories based on controlling the steering angle rate and the braking rate at the vehicle’s limits. Occupant injury severity prediction, based on accident data with the focus on impact location, is used by a real-time collision control algorithm to choose a trajectory from the pre-computed optimal set. A simulation set-up is presented to illustrate the idea of the collision control algorithm in a simple scenario involving dynamic traffic agents.
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| 2. |
- Krook, Jonas, 1986, et al.
(author)
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Design and Formal Verification of a Safe Stop Supervisor for an Automated Vehicle
- 2019
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In: Proceedings - IEEE International Conference on Robotics and Automation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1050-4729. - 9781538660263 ; 2019-May, s. 5607-5613
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Conference paper (peer-reviewed)abstract
- Autonomous vehicles apply pertinent planning and control algorithms under different driving conditions. The mode switch between these algorithms should also be autonomous. On top of the nominal planners, a safe fallback routine is needed to stop the vehicle at a safe position if nominal operational conditions are violated, such as for a system failure. This paper describes the design and formal verification of a supervisor to manage all requirements for mode switching between nominal planners, and additional requirements for switching to a safe stop trajectory planner that acts as the fallback routine. The supervisor is designed via a model-based approach and its abstraction is formally verified by model checking. The supervisor is implemented and integrated with the Research Concept Vehicle, an experimental research and demonstration vehicle developed at the KTH Royal Institute of Technology. Simulations and experiments show that the vehicle is able to autonomously drive in a safe manner between two parking lots and can successfully come to a safe stop upon GPS sensor failure.
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| 3. |
- Svensson, Lars, 1989-, et al.
(author)
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Adaptive Trajectory Planning and optimization at Limits of Handling
- 2019
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In: IEEE International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728140049 ; , s. 3942-3948
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Conference paper (peer-reviewed)abstract
- In this paper, we tackle the problem of trajectory planning and control of a vehicle under locally varying traction limitations, in the presence of suddenly appearing obstacles. We employ concepts from adaptive model predictive control for run-time adaptation of tire force constraints that are imposed by local traction conditions. To solve the resulting optimization problem for real-time control synthesis with such time varying constraints, we propose a novel numerical scheme based on Real Time Iteration Sequential Quadratic Programming (RTI-SQP), which we call Sampling Augmented Adaptive RTI (SAA-RTI). Sampling augmentation of conventional RTI-SQP provides additional feasible candidate trajectories for warm-starting the optimization procedure. Thus, the proposed SAA-RTI algorithm enables real time constraint adaptation and reduces sensitivity to local minima. Through extensive numerical simulations we demonstrate that our method increases the vehicle's capacity to avoid accidents in scenarios with unanticipated obstacles and locally varying traction, compared to equivalent non-adaptive control schemes and traditional planning and tracking approaches.
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