Real Time Estimation of Drivers' Behaviour

Monteil, Julien; O'Hara, Niall; Cahill, Vinny; Bouroche, Mélanie

doi:10.1109/itsc.2015.331

Cited by 29 publications

(17 citation statements)

References 23 publications

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“…Due to the view obstructed by the buildings (hatched boxes), an infrastructure sensor system is required to provide the headway data from the major street to the CAV. [1] also can be derived by this procedure as no classical model is able to reproduce real world vehicle-following behaviour in an appropriate way [19,20].…”

Section: Figurementioning

confidence: 99%

Correlations in Spatiotemporal Headway Dynamics of Road Traffic Using Extremely Accurate Microscopic Empirical Data

Kumm

Schreckenberg

2020

Coll Dyn

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As we recently showed by using empirical data there is a certain behavior referring to the development of the headway between two consecutive human driven vehicles. Following on from this, we investigate correlations of the change in temporal headway over two subsequent road segments as the main goal of the present work and found a strongly correlated behaviour for increasing temporal headways. In this way a strong improvement for short-term prediction algorithms of conventional road users should be achieved. A stationary infrared-based sensor system was developed for this purpose, which has been mounted at reflector posts next to an urban street over a distance of about 50m. Due to its good accuracy, we are able to resolve vehicle following times down to 25 milliseconds and to determine speeds more precisely. In 45 hours of measurement the system detected over 20,000 passing vehicles.

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Section: Figurementioning

confidence: 99%

Correlations in Spatiotemporal Headway Dynamics of Road Traffic Using Extremely Accurate Microscopic Empirical Data

Kumm

Schreckenberg

2020

Coll Dyn

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“…Looking at equation (3), the acceleration and speed of vehicle n, and the relative positions and speeds between vehicle n and vehicle n−1 need to be tracked to be able to estimate θ n via static parameter estimation techniques, see e.g. Monteil et al (2015). Considering that only the positions and speeds of 2 upstream and downstream vehicles can be tracked with in-vehicle sensors, we rarely have i < n − 1 and j > n + 2 unless behavioural parameter data is transmitted via communication channels.…”

Section: Optimisation Problemmentioning

confidence: 99%

$\mathcal{L}_2$ and $\mathcal{L}_{\infty}$ Stability Analysis of Heterogeneous Traffic With Application to Parameter Optimization for the Control of Automated Vehicles

Monteil

Bouroche

Leith

2019

IEEE Trans. Contr. Syst. Technol.

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The presence of (partially) automated vehicles on the roads presents an opportunity to compensate the unstable behaviour of conventional vehicles. Vehicles subject to perturbations should (i) recover their equilibrium speed, (ii) react not to propagate but absorb perturbations. In this work, we start with considering vehicle systems consisting of heterogeneous vehicles updating their dynamics according to realistic behavioural car-following models. Definitions of all types of stability that are of interest in the vehicle system, namely input-output stability, scalability, weak and strict string stability, are introduced based on recent studies. Then, frequency domain linear stability analyses are conducted after linearisation of the modelled system of vehicles, leading to conditions for input-output stability, strict and weak string stability over the behavioural parameters of the system, for finite and infinite systems of homogeneous and heterogeneous vehicles. This provides a solid basis that was missing for car-following model-based control design in mixed traffic systems where only a proportion of vehicles can be controlled. After visualisation of the theoretical results in simulation, we formulate an optimisation strategy with LMI constraints to tune the behavioural parameters of the automated vehicles in order to maximise the L ∞ string stability of the mixed traffic flow while considering the comfort of automated driving. The optimisation strategy systematically leads to increased traffic flow stability. We show that very few automated vehicles are required to prevent the

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“…It was recently shown in Monteil et al and Punzo et al that, under dense traffic conditions with speeds (eg, around 17 m/s in highways) that are far enough from the desired speeds (ie, the maximum speed limit, here, 33 m/s), only 2 parameters are sensitive and therefore need to be estimated: the desired time headway T h and the maximum tolerated acceleration a c c . These 2 parameters are therefore the parameters we will consider for reconfiguration at runtime in this paper.…”

Section: Background Information and Motivationmentioning

confidence: 99%

“…These are the parameters more affected by adverse conditions. () In summary, as the intensity of rain develops, acceleration is reduced and time headway is increased…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…It was recently shown in Monteil et al 14 and Punzo et al 15 that, under dense traffic conditions with speeds (eg, around 17 m/s in highways) that are far enough from the desired speeds (ie, the maximum speed limit, here, 33 m/s), only 2 parameters are sensitive and therefore need to be estimated:…”

Section: Relationships Between Car-following Behavioural Parameters Amentioning

confidence: 99%

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Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

Horcas

Monteil

Bouroche

et al. 2017

J Software Evolu Process

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Human drivers naturally adapt their behaviour depending on the traffic conditions, such as the current weather and road type. Autonomous vehicles need to do the same, in a way that is both safe and efficient in traffic composed of both conventional and autonomous vehicles. In this paper, we demonstrate the applicability of a reconfigurable vehicle controller agent for autonomous vehicles that adapts the parameters of a used car‐following model at runtime, so as to maintain a high degree of traffic quality (efficiency and safety) under different weather conditions. We follow a dynamic software product line approach to model the variability of the car‐following model parameters, context changes and traffic quality, and generate specific configurations for each particular context. Under realistic conditions, autonomous vehicles have only a very local knowledge of other vehicles' variables. We investigate a distributed model predictive controller agent for autonomous vehicles to estimate their behavioural parameters at runtime, based on their available knowledge of the system. We show that autonomous vehicles with the proposed reconfigurable controller agent lead to behaviour similar to that achieved by human drivers, depending on the context.

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Real Time Estimation of Drivers' Behaviour

Cited by 29 publications

References 23 publications

Correlations in Spatiotemporal Headway Dynamics of Road Traffic Using Extremely Accurate Microscopic Empirical Data

Correlations in Spatiotemporal Headway Dynamics of Road Traffic Using Extremely Accurate Microscopic Empirical Data

$\mathcal{L}_2$ and $\mathcal{L}_{\infty}$ Stability Analysis of Heterogeneous Traffic With Application to Parameter Optimization for the Control of Automated Vehicles

Context‐dependent reconfiguration of autonomous vehicles in mixed traffic

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