A Cooperative Car-Following/Emergency Braking System With Prediction-Based Pedestrian Avoidance Capabilities

Flores, Carlos; Merdrignac, Pierre; Charette, Raoul de; Navas, Francisco; Milanés, Vicente; Nashashibi, Fawzi

doi:10.1109/tits.2018.2841644

Cited by 49 publications

(31 citation statements)

References 39 publications

(45 reference statements)

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“…Subsection VI-A demonstrates the accuracy of the analysis of standstill spacing and braking duration by the DDEs, including the crossing criterion and the determination of d end and t end based on RK4. Next, in subsection VI-B, the improved performance of the proposed RSD algorithm is presented via comparison with four related algorithms, namely, the car-following algorithm [25], the feedback control algorithm [26], the maximum braking algorithm [27], and the fixed setting algorithm using the same control laws [6]. Finally, in subsection VI-C, the relationship among the limits of the stable velocity, the delay and the stable distance is presented as a reference for platoon control while ensuring safety.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The conclusion can be drawn that the DDE (14) is noncrossing and decreasing, i.e., the braking process is Braking scenario 1, if Eqs. (24) and (25) are satisfied.…”

Section: B Crossing Criterion For Differentiating Braking Scenariosmentioning

confidence: 97%

“…In a previous study, an experimental platform and an actual-vehicle experiment were set up to evaluate emergency braking [24], but delay was not considered. Several works have considered delays in emergency braking [25]- [27]. For instance, Flores et al [25] presented a cooperative collision avoidance system based on pedestrian prediction, in which delay was considered merely in the correction of velocity, and in-vehicle safety was ignored.…”

Section: Related Workmentioning

confidence: 99%

“…Several works have considered delays in emergency braking [25]- [27]. For instance, Flores et al [25] presented a cooperative collision avoidance system based on pedestrian prediction, in which delay was considered merely in the correction of velocity, and in-vehicle safety was ignored. Xu et al [26] compared different communication information structures and contents to analyze the intrinsic relationships between coordination and communication in platoons and enhance platoon safety, including during slow and fast braking.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the performance of the proposed RSD algorithm, it is compared with four related algorithms, namely, the carfollowing algorithm [25], the feedback control algorithm [26] the maximum braking algorithm [27], and the fixed setting algorithm, which has the same control laws as the RSD algorithm. The parameters are listed in Table 2, and the parameters of the fixed setting algorithm are set as a = 4, b = 0.6, d sparse = 35 m, and d dense = 1.5d safe /d dense = 2d safe [6].…”

Section: B the Performance Of The Proposed Rsd Algorithmmentioning

confidence: 99%

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Reducing the Standstill Spacing and Duration of Safe Braking Control in Vehicle Platoons With Delays

Meng

Wang

2020

IEEE Access

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Braking control, especially in an emergency, is a key technology that is needed to ensure safety in vehicle platoons. However, delays in vehicle platoons can severely affect braking control. This paper proposes an optimized braking control to reduce the standstill spacing and braking duration during delays so that a platoon will stop within a short time frame with a reduced length, thus improving road utilization while ensuring both inter-vehicle and in-vehicle safety. However, two challenges need to be addressed. First, due to the delay in car-following interactions and the nonlinearity of the control law, an analysis model is needed to quantize the duration and distance during an emergency braking with delays. Second, the optimization of these control parameters is an NP-hard problem. Therefore, delay differential equations are introduced to model the braking process, and a crossing criterion is introduced to establish the relationship between the control law and the braking process. The propositions of standstill spacing and braking duration are then derived based on the Runge-Kutta method. According to these criteria, a particle swarm optimization (PSO) based on a lexicographic method with a penalty function is introduced to provide a solution framework with polynomial complexity. Simulation results verify the accuracy of the braking modeling process. Moreover, the results verify the performance of the proposed algorithm and provide a reference for platoon control.

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Section: Simulation Resultsmentioning

confidence: 99%

“…The conclusion can be drawn that the DDE (14) is noncrossing and decreasing, i.e., the braking process is Braking scenario 1, if Eqs. (24) and (25) are satisfied.…”

Section: B Crossing Criterion For Differentiating Braking Scenariosmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B the Performance Of The Proposed Rsd Algorithmmentioning

confidence: 99%

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Reducing the Standstill Spacing and Duration of Safe Braking Control in Vehicle Platoons With Delays

Meng

Wang

2020

IEEE Access

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A game theory‐based route planning approach for automated vehicle collection

Hadded

Minet

Lasgouttes

2021

Concurrency and Computation

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We consider a shared transportation system in an urban environment where human drivers collect vehicles that are no longer being used. Each driver, also called a platoon leader, is in charge of driving collected vehicles as a platoon to bring them back to some given location (e.g. an airport, a railway station). Platoon allocation and route planning for picking up and returning automated vehicles is one of the major issues of shared transportation systems that need to be addressed. In this paper, we propose a coalition game approach to compute 1) the allocation of unused vehicles to a minimal number of platoons, 2) the optimized tour of each platoon and 3) the minimum energy consumed to collect all these vehicles. In this coalition game, the players are the parked vehicles, and the coalitions are the platoons that are formed. This game, where each player joins the coalition that maximizes its payoff, converges to a stable solution. The quality of the solution obtained is evaluated with regard to three optimization criteria and its complexity is measured by the computation time required. Simulation experiments are carried out in various configurations. They show that this approach is very efficient to solve the multi-objective optimization problem considered, since it provides the optimal number of platoons in less than a second for 300 vehicles to be collected, and considerably outperforms other well-known optimization approaches like MOPSO (Multi-Objective Particle Swarm Optimization) and NSGA-II (Non dominated Sorting Genetic Algorithm).

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Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

Taran

Gordienko

Rokovyi

et al. 2019

Advances in Intelligent Systems and Computing

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Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union -mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1the fine GT images for training and prediction, and scenario 2the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.

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A Cooperative Car-Following/Emergency Braking System With Prediction-Based Pedestrian Avoidance Capabilities

Cited by 49 publications

References 39 publications

Reducing the Standstill Spacing and Duration of Safe Braking Control in Vehicle Platoons With Delays

Reducing the Standstill Spacing and Duration of Safe Braking Control in Vehicle Platoons With Delays

A game theory‐based route planning approach for automated vehicle collection

Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

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