Longitudinal Model Predictive Control with comfortable speed planner

Matute, José A.; Marcano, Mauricio; Pérez, Joshue

doi:10.1109/icarsc.2018.8374161

Cited by 23 publications

(13 citation statements)

References 16 publications

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“…Its task is to move the actuator of the automated vehicle, which means, steering wheel and pedals. Characterization of the actuators is the proposed architecture, as presented in [26].…”

Section: Actuationmentioning

confidence: 99%

A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Matute

Pérez

2020

Sensors

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Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution.

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“…Its task is to move the actuator of the automated vehicle, which means, steering wheel and pedals. Characterization of the actuators is the proposed architecture, as presented in [26].…”

Section: Actuationmentioning

confidence: 99%

A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Matute

Pérez

2020

Sensors

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“…2) Triple Integrator Model for the Decoupled Controller: A triple-integrator is implemented to model the longitudinal kinematic of the vehicle in the proposed decoupled controller [7]. In this approach, the vehicle is considered as an ideal particle which is considered as an ideal particle traveling along a predefined route.…”

Section: Vehicle Motion Controlmentioning

confidence: 99%

“…In contrast with previous strategies, the Model Predictive Control (MPC) permits to constraint physical parameters assuring safety and under a desired level of comfort [7]. Being a complex control problem, two strategies have been proposed when implementing the motion controllers.…”

Section: Introductionmentioning

confidence: 99%

“…This way, in this work a comparative study is carried out considering two predictive controllers that consider comfort constraints in their formulations: a coupled lateral and longitudinal motion controller based on a modification of the kinematic bicycle model MPC proposed in [8]; and a decoupled approach that combines a triple-integrator model based MPC [7] for the lateral control combined with a double proportional curvature control for the lateral control [12]. The performance of both controllers and the coupling effect is analyzed considering a set of simulations using a realistic simulation environment, in which both the test vehicle and its low-level control have been modelled.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models

Matute

Lattarulo

Pérez

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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In this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.

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“…The virtual platform is developed in Dynacar, a simulation tool for vehicle dynamics based on a multi-body formulation [20]. This formulation is very uselful to obtain different physical parameters, being mainly important the study of passeger's stability and comfort [21]. A module within this tool is dedicated to develop 3D environments as well as perform test visualizations.…”

Section: A Dynacar As Simulation Environmentmentioning

confidence: 99%