Cooperative Driving With a Heavy-Duty Truck in Mixed Traffic: Experimental Results

Nieuwenhuijze, Maarten R. I.; Keulen, Thijs van; Oncu, Sinan; Bonsen, B.; Nijmeijer, Henk

doi:10.1109/tits.2012.2202230

Cited by 86 publications

(33 citation statements)

References 8 publications

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“…This competition revealed some of the most important problems to be solved before bringing this technology into production, including the communication systems reliability. From the control point of view, most of the implementations were based on proportional, proportional-derivative feedback/feedforward controllers [13], [14], [15] or Model Predictive Control (MPC) techniques [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Adaptive Cruise Control in Real Traffic Situations

Milanés

Shladover

Spring

et al. 2014

IEEE Trans. Intell. Transport. Syst.

897

429

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Abstract-Intelligent vehicle cooperation based on reliable communication systems contributes not only to reducing traffic accidents, but also to improving traffic flow. Adaptive Cruise Control (ACC) systems can gain enhanced performance by adding vehicle-vehicle wireless communication to provide additional information to augment range sensor data, leading to Cooperative ACC (CACC). This paper presents the design, development, implementation and testing of a CACC system. It consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon. The system has been implemented on four production Infiniti M56s vehicles, and this paper details the results of experiments to validate the performance of the controller and its improvements with respect to the commercially available ACC system.

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Section: Introductionmentioning

confidence: 99%

Cooperative Adaptive Cruise Control in Real Traffic Situations

Milanés

Shladover

Spring

et al. 2014

IEEE Trans. Intell. Transport. Syst.

897

429

View full text Add to dashboard Cite

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“…Using this additional information, control system performance can be tightened significantly, enabling significant reductions in inter-vehicle distance and improvements in string stability [25]. Recently, its potential benefits were tested in the Grand Cooperative Driving Challenge held in Helmond, The Netherlands in 2011 [32] where nine vehicles from different European research institutions drove in a two-lane platoon using different control techniques mainly based on proportional, or proportional-derivative feedback/feedforward controllers [9,17,4] or Model Predictive Control (MPC) techniques [3,8]. A successful demonstration was also carried out by The Connect & Drive project using a six-vehicle platoon [18] in The Netherlands.…”

Section: Introductionmentioning

confidence: 99%

Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

Milanés¹,

Shladover²

2014

Transportation Research Part C: Emerging Technologies

778

411

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Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: 1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; 2) consecutive strings of ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and 3) consecutive strings of CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.

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“…In such formations, the main goal is to maintain a desirably small spacing between the vehicles in a way to increase traffic capacity and reduce fuel consumption (see [22], [17], [1], [2], [18] and the references therein). To avoid congestion, it is necessary to prevent the backward amplification of spacing errors initiated by a maneuver (deceleration or acceleration) of the vehicle that leads the platoon.…”

Section: Introductionmentioning

confidence: 99%

Joint synthesis of dynamic feed-forward and static state feedback for platoon control

Köroğlu

Falcone

2014

53rd IEEE Conference on Decision and Control

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Joint synthesis of static state-feedback and dynamic feed-forward is considered for a homogenous platoon operating under the leader and predecessor following scheme. The problem is formulated with two H ∞ -type performance objectives. The first objective requires the first vehicle to respond in a desirable way to the maneuvers of the leader and it is to be realized by a joint synthesis of the feed-forward filter together with a state feedback gain. Novel LMI conditions are derived for this objective by fixing the order and the poles of the filter. The second objective aims at preventing the amplification of the errors backward along the platoon, while also maintaining robustness against communication problems with the leader. New sufficient LMI conditions are provided for this objective, based on which leader and predecessor feedback gain matrices can be constructed. 53rd IEEE Conference on Decision and Control

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Cooperative Driving With a Heavy-Duty Truck in Mixed Traffic: Experimental Results

Cited by 86 publications

References 8 publications

Cooperative Adaptive Cruise Control in Real Traffic Situations

Cooperative Adaptive Cruise Control in Real Traffic Situations

Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

Joint synthesis of dynamic feed-forward and static state feedback for platoon control

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