Scenario-based model predictive speed controller considering probabilistic constraint for driving scene with pedestrian

Muraleedharan, Arun; Tran, Anh-Tuan; Okuda, Hiroyuki; Suzuki, Tatsuya

doi:10.1109/itsc45102.2020.9294176

Cited by 4 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To compute the prediction of the future states of the pedestrians, we refer to the model in [20], which reflects the model used for the TV. The pedestrian state is ξ P = [x P , v P…”

Section: Pedestrian Modelmentioning

confidence: 99%

“…Note that (20) is not needed for safety, guaranteed by the analogous condition (13) in the low level, but rather to make the maneuver planner aware of the future position of the TV, allowing to plan a smooth deceleration when needed.…”

Section: ) Tv In Frontmentioning

confidence: 99%

“…where ν −1 is set equal to the current speed v 0 , and set Z in (23e) compactly represents constraints (18), (20), and (22). The cost function (23a) is designed to penalize large accelerations, for sake of comfort and coherently with (16a), and to penalize deviations from a desired cruise speed.…”

Section: Optimal Control Problemmentioning

confidence: 99%

See 2 more Smart Citations

Multistage Stochastic Model Predictive Control for Urban Automated Driving

Benciolini,

Brüdigam,

Leibold

2021

Preprint

View full text Add to dashboard Cite

Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result in overly conservative motion planning. Hence, we propose to use Stochastic Model Predictive Control for vehicle control in urban driving, allowing to efficiently plan the vehicle trajectory, while maintaining the risk probability sufficiently low. For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately. A highlevel layer takes advantage of a long prediction horizon and of an abstract model to plan the optimal maneuver, and a lower level is in charge of executing the selected maneuver by properly planning the vehicle's trajectory. Numerical simulations are included, showing the potential of our proposal.

show abstract

“…To compute the prediction of the future states of the pedestrians, we refer to the model in [20], which reflects the model used for the TV. The pedestrian state is ξ P = [x P , v P…”

Section: Pedestrian Modelmentioning

confidence: 99%

Section: ) Tv In Frontmentioning

confidence: 99%