Determination of the order of electronically coupled trucks on German motorways

Kunze, Ralph; Tummel, Christian; Henning, Klaus

doi:10.1109/peits.2009.5406751

Cited by 9 publications

(4 citation statements)

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“…The working distance of the infrared lights limits the distance between preceding and following vehicle to about 3m. In AHS however, the distance should be around 10m or more [8] [7].…”

Section: State Of the Artmentioning

confidence: 99%

A mechanism to improve Stereo Vision Systems in automated heterogeneous platoons

Alfraheed¹,

Droge²,

Klingender³

et al. 2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Due to their low price and good quality, Stereo Vision Systems (SVS) are recently considered as a key factor to gather actual information about the object of interest. Today, automated highway systems (AHS) for urban and highway environment were developed without the use of a stereo vision system. In future, the application of AHS should be extended to unstructured environments (e.g. desert) and be adapted to heterogeneous vehicles. In this context, the stereo vision system could enable the platoon to be independent from environmental structure (e.g. lane markings) through its ability to detect, track, locate and recognize heterogeneous vehicles. So far, the need for high accuracy prevents SVS to be applied in automated heterogeneous platoon. In this paper a mechanism towards this is presented, where some behavioral properties have to be satisfied in terms of unstructured environment and heterogeneous platoons. Within a heterogeneous platoon, the back view of a preceding vehicle (BVPV) is considered as a reference point for the lateral and longitudinal control. The key idea of the proposed mechanism is to confirm that the distance of the BVPV can be extracted without depending on the movement of the preceding vehicle. Furthermore, the proposed mechanism has to ensure that features extracted from the back view are suitable to implement successfully the calibration process at around 10m distance. With the proposed SVS mechanism some of behavioral properties have to be satisfied in terms of unstructured environment and AHS. These properties are reliability, performance and robustness. Compared to other methods which use a SVS, the proposed mechanism distinguishes itself through adapting to dynamic environment and extracting the necessary features for the calibration process.

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“…The working distance of the infrared lights limits the distance between preceding and following vehicle to about 3m. In AHS however, the distance should be around 10m or more [8] [7].…”

Section: State Of the Artmentioning

confidence: 99%

A mechanism to improve Stereo Vision Systems in automated heterogeneous platoons

Alfraheed¹,

Droge²,

Klingender³

et al. 2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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show abstract

“…Similar projects have also been carried out in other countries, such as California PATH, Energy-ITS, KONVOI, SARTRE, etc. [2][3][4][5][6]. The focus of these projects is mostly on truck control methods, platoon stability, and the external communication of trucks; all require automatic driving strategies to control all or some of the trucks in the platoon.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, refs. [2,4,5] achieved spacings of 6 m, 10 m, and 4.7 m, respectively. However, this method does not minimize the total energy consumption when the speed varies, as the platoon might need more energy to keep the preset spacing.…”

Section: Introductionmentioning

confidence: 99%

Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions

Chen,

Zhou,

Duan

et al. 2023

WEVJ

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With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%.

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“…Therefore, several projects concerning Automated Highway Systems (AHS) were initiated [1] [2]. With AHS two homogenous vehicles are coupled electronically in order to optimize the highway capacity [3][4].…”

Section: Introductionmentioning

confidence: 99%

Real time detection of the back view of a preceding vehicle for automated heterogeneous platoons in unstructured environment using video

Alfraheed

Droge

Kunze

et al. 2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Due to the increase in road transportation several projects concerning automated highway systems were initiated to optimize highway capacity. In the future, the developed techniques should be applicable in unstructured environment (e.g. desert) and adaptable for heterogeneous vehicles. But before, several challenges, i.e. independency of lane markings, have to be overcome. Our solution is to consider the back view of the preceding vehicle as a reference point for the lateral and longitudinal control of the following vehicle. This solution is independent from the environmental structure as well as additional equipment like infrared emitters. Thus, both the detection and tracking process of the back view are needed to provide automated highway systems with the distance and the deviation degree of the preceding vehicle. In this paper the first step, the detection and location of the back view on video streams, is discussed. For a definite detection in a heterogeneous platoon several features of the back view are detected. A method is proposed to run rejection cascades generated by the AdaBoost classifier theory on the video stream. Compared to other methods related to object detection, the proposed method reduces the running time for the detection of the back view to 0.03-0.08 s/frame. Furthermore, the method enables a more accurate detection of the back view.

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Determination of the order of electronically coupled trucks on German motorways

Cited by 9 publications

References 0 publications

A mechanism to improve Stereo Vision Systems in automated heterogeneous platoons

A mechanism to improve Stereo Vision Systems in automated heterogeneous platoons

Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions

Real time detection of the back view of a preceding vehicle for automated heterogeneous platoons in unstructured environment using video

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