| 1. |
- Lima, Pedro F., 1990-, et al.
(författare)
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Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck
- 2017
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 2:4, s. 238-250
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present an algorithm for lateral control of a vehicle – a smooth and accurate model predictive controller. The fundamental difference compared to a standard MPC is that the driving smoothness is directly addressed in the cost function. The controller objective is based on the minimization of the first- and second-order spatial derivatives of the curvature. By doing so, jerky commands to the steering wheel, which could lead to permanent damage on the steering components and vehicle structure, are avoided. A good path tracking accuracy is ensured by adding constraints to avoid deviations from the reference path. Finally, the controller is experimentally tested and evaluated on a Scania construction truck. The evaluation is performed at Scania’s facilities near So ̈derta ̈lje, Sweden via two different paths: a precision track that resembles a mining scenario and a high-speed test track that resembles a highway situation. Even using a linearized kinematic vehicle to predict the vehicle motion, the performance of the proposed controller is encouraging, since the deviation from the path never exceeds 30 cm. It clearly outperforms an industrial pure-pursuit controller in terms of path accuracy and a standard MPC in terms of driving smoothness.
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| 2. |
- Ward, Erik, et al.
(författare)
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Probabilistic Model for Interaction Aware Planning in Merge Scenarios
- 2017
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 2:2, s. 133-146
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Tidskriftsartikel (refereegranskat)abstract
- Merge scenarios confront drivers with some of the most complicated driving maneuvers in every day driving, requiring anticipatory reasoning of positions of other vehicles, and the own vehicles future trajectory. In congested traffic it might be impossible to merge without cooperation of up-stream vehicles, therefore, it is essential to gauge the effect of our own trajectory when planning a merge maneuver. For an autonomous vehicle to perform a merge maneuver in congested traffic similar capabilities are required. This includes a model describing the future evolution of the scene that allows for optimizing the autonomous vehicle's planned trajectory with respect to risk, comfort, and dynamical limitations. We present a probabilistic model that explicitly models interaction between vehicles and allows for evaluating the utility of a large number of candidate trajectories of an autonomous vehicle using a receding horizon approach in order to select an appropriate merge maneuver. The model is an extension of the intelligent driver model and the modeled behavior of other vehicles are adjusted using on-line model parameter estimation in order to give better predictions. The prediction model is evaluated using naturalistic traffic data and the merge maneuver planner is evaluated in simulation.
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| 3. |
- Åsljung, Daniel, 1989, et al.
(författare)
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Using Extreme Value Theory for Vehicle Level Safety Validation and Implications for Autonomous Vehicles
- 2017
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 2:4, s. 288-297
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Tidskriftsartikel (refereegranskat)abstract
- Much effort is put right now into how to make autonomous vehicles as capable as possible in order to be able to replace humans as drivers. Less focus is put into how to ensure that this transition happens in a safe way that we can put trust in. The verification of the extreme dependability requirements connected to safety is expected to be one of the largest challenges to overcome in the commercialization of autonomous vehicles. Using traditional statistical methods to validate complete vehicle safety would require the vehicle to cover extreme distances to show that collisions occur rare enough. However, recent research has shown the possibility of using near-collisions in order to estimate the frequency of actual collisions using Extreme Value Theory. To use this method, there is a need for a measure related to the closeness of a collision. This paper shows that the choice of this threat measure has a significant impact on the inferences drawn from the data. With the right measure, this method can be used to validate the safety of a vehicle. This, while keeping the validity high and the data required lower than the state of the art statistical methods.
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