Cooperative Intersection Support System Based on Mirroring Mechanisms Enacted by Bio-Inspired Layered Control Architecture

Lio, Mauro Da; Mazzalai, Alessandro; Darin, Marco

doi:10.1109/tits.2017.2731901

Cited by 11 publications

(9 citation statements)

References 51 publications

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“…In the driving domain, optimal control has been used to describe driver behaviors (from a functional point of view) both for long-term tactical plans [43]- [45] and for producing atomic motor units (motor primitives) for layered control architectures [46]- [48]. However, the connection with studies in human optimal sensory-motor control have been rarely recognized.…”

Section: B Biological Plausible Human Behavioral Modelsmentioning

confidence: 99%

“…Thus, for what concerns the integral part, formulation (4) is identical to previously used ones [46]- [48]. The difference in this paper is the new term w A a 2 f ; a terminal cost which is introduced to reduce the final acceleration, a f , making it loosely bound to zero, instead of exactly zero.…”

Section: Final Conditions (For Free Flow)mentioning

confidence: 99%

“…More complex situations call for more complex selection mechanisms [49], requiring the representation of actions in a metric space, and the use of inhibition mechanisms. A discussion of action selection mechanisms for such more general situations in longitudinal control is given in [48], section V.B.…”

Section: Final Conditions (For Free Flow)mentioning

confidence: 99%

“…The similarity of sensorimotor control sought in this paper approach is indeed finalized to mirroring (both robot mirroring the human and vice-versa). We provide here one example; however, on this aspect the reader is invited to consider our previous work which present more extensive explanations [46]- [48].…”

Section: Comparison With the Intelligent Driver Modelmentioning

confidence: 99%

“…HUMAN-DIRECTED JERK Moreover, for Model 1 we use the parametrization of [48] (w T = 0, w A = ∞, instead of the figures given in Table I 2 ) for comparison with that work, which further develops the notion of mirroring in intersection scenarios. We call this Model 1 * .…”

Section: Table IVmentioning

confidence: 99%

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Biologically Guided Driver Modeling: the Stop Behavior of Human Car Drivers

Lio

Mazzalai

Gurney

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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This paper presents a principled approach to the modeling of human drivers-applied to stop behavior-by uniting recent ideas in cognitive science and optimal control. With respect to the former, we invoke the affordance competition hypothesis, according to which human behavior is produced by resolving the competition between action affordances that are simultaneously instantiated in response to the environment. From the theory of optimal control, we deploy motor primitives based on minimum jerk as the potential suite of actions. Furthermore, we invoke a layered control architecture, which carries out action priming and action selection sequentially, to model the biological affordance competition process. Motor output may be directed to distinct motor channels, which may be partially inhibited, e.g., to model gas pedal release saturation. Within this architecture, two types of motor units-"deceleration" acting on a gas pedal channel and "brake" acting on a brake pedal channel-are sufficient to model, with remarkable accuracy, the various phases that can be observed in human maneuvers in stopping a car, namely: gas release, gas chocked, brake, and final brake release at stop. The model is validated using experimental data collected in 16 different stop locations, from roundabouts to traffic lights. We also carry out a comparison with the well-known Intelligent Driver Model, discuss the scaling of this framework to more general driving scenarios and finally give an example application where the driver model is used, within a mirroring process, to infer the human driver intentions.

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Section: B Biological Plausible Human Behavioral Modelsmentioning

confidence: 99%

Section: Final Conditions (For Free Flow)mentioning

confidence: 99%

Section: Final Conditions (For Free Flow)mentioning

confidence: 99%

Section: Comparison With the Intelligent Driver Modelmentioning

confidence: 99%

Section: Table IVmentioning

confidence: 99%

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Biologically Guided Driver Modeling: the Stop Behavior of Human Car Drivers

Lio

Mazzalai

Gurney

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Stability Design for the Homogeneous Platoon with Communication Time Delay

Wang

Yang

et al. 2020

Automot. Innov.

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A hierarchical control framework is applied for the distributed cooperative vehicular platoon using vehicular ad-hoc networks. The parameter-space-approach-based cooperative adaptive cruise control (CACC) controller is proposed to guarantee the $${\mathcal{D}}$$D-stability and the string stability considering the influence of the communication time delay and time lag of vehicular dynamic performance. This CACC controller combines the feedforward loop of the acceleration of the preceding vehicle with the feedback loop of the following errors, in which the gain of the feedforward loop is designed to decrease matching errors and the gains of the feedback loop are selected from the feasible region in the parameter space. To verify the effectiveness of the CACC controller, a six-vehicle platoon with a simplified vehicular dynamic is simulated under speed-up and stop scenarios. The simulation results demonstrate that the disturbance is attenuated along with the platoon and the following errors are convergent with well-designed convergent performance. A CarSim/Simulink co-simulation is designed to further verify the effectiveness of the hierarchical control framework and the rationality of the CACC controller in the real vehicular platoon application. The simulation results under the highway fuel economy test drive cycle show that the CACC controller improves the drive comfort and significantly decreases the following errors.

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