Robust Distributed Consensus Control of Uncertain Multiagents Interacted by Eigenvalue-Bounded Topologies

Li, Keqiang; Li, Shengbo Eben; Gao, Feng; Lin, Ziyu; Li, Jie; Sun, Qi

doi:10.1109/jiot.2020.2973927

Cited by 38 publications

(19 citation statements)

References 15 publications

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“…How to construct is investigated in Section III. Here the transition logic for lateral and longitudinal cooperation are conducted separately, but they have the same formula as (1). This implies that the steering is controlled by the automatic driving system, while the powertrain/braking may still be manipulated manually.…”

Section: Driving Capability-based Transition Strategymentioning

confidence: 99%

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Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

2020

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In open traffic environments, humans still have to remain in the control loop of vehicle due to the insufficient of the existing technologies and their high costs. For the realization of cooperation between the human and the automatic driving system, the determination of the time when automatic driving is necessary is very important. To avoid unnecessary intervention when the driver has the control authority of vehicle, a new driving capability-based transition strategy was proposed, which comprehensively considers the driver's correction ability and the driving risk. The transition time from the human driver to the automatic driving system is determined by an unreliable domain (UD), whose boundary is modeled according to the driving data recorded by a driving simulator and statistically described by a log-normal distribution. Furthermore, an adaptive algorithm is designed to update the parameters of UD boundary online to make this strategy suitable for different drivers. This UD-based transition strategy is validated by several tests on the driving simulator. The bench test results show that the individual driving characteristic can be identified by the adaptive algorithm in time, the transition time determined by UD is more accurate, and sufficient time is reserved for the correction carried out by the automatic driving system. INDEX TERMS Automatic driving, human-machine cooperation, driving capability, correction ability, driving risk.

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Section: Driving Capability-based Transition Strategymentioning

confidence: 99%

“…Traffic safety has always been an area of social concern, especially with the ever-increasing number of vehicles [1]. Driver error, the result of visual distraction or fatigue, is considered as a major contributor to traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

2020

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“…Compared with traditional onboard systems, automatic driving brings much better social and economic benefits, so it becomes a hot spot for research and production [1][2][3]. Many competitions have been held to promote its commercialisation, for example, the Grand Challenge sponsored by the Defense Advanced Research Projects Agency [4] and the Intelligent Vehicle Future Challenges sponsored by the National Science Foundation of China [5].…”

Section: Introductionmentioning

confidence: 99%

Evolution test by improved genetic algorithm with application to performance limit evaluation of automatic parallel parking system

Gao

Zhang

Han

et al. 2021

IET Intelligent Trans Sys

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Performance limit evaluation of automatic driving system before putting into the market is critical for driving safety. The evolution test by genetic algorithm (GA) is a method by iteratively generating new test scenarios according to the last test results. To avoid its blind search for better efficiency, a scenario complexity index is proposed to measure the test effectiveness indirectly and guide the evolution process under the assumption that a complex scenario is more challenging to realise automatic driving. The traditional crossover and mutation operators are modified to generate more complex scenarios to improve the test efficiency. The advantage of the improved crossover/mutation operators in increasing the offspring's scenario complexity index is analysed in theory. Moreover, the influence of the design parameters on the evolution test process and the global convergence are also discussed. The new evolution test by this improved GA has been applied to find the collision condition of a parallel automatic parking system to validate its effectiveness.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Over the past decade, there have been significant researches on this area [1], including the detection of pedestrian [2], traffic sign [3] [4], vehicle [5] [6], traffic light [7] and etc. The objective of detection system is to provide the driver or automatic driving system with timely and accurate information about the environment to save lives by reducing the number of traffic accidents [8] [9].…”

Section: Introductionmentioning

confidence: 99%

A Combined Object Detection Method With Application to Pedestrian Detection

Gao

Wang

2020

IEEE Access

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Object detection plays an important role in automatic driving systems. Considering the characteristics of classical and deep learning algorithms, a fusion logic is proposed to combine the advantages of these two kinds of object detectors. The relationship of detection performance among different detectors is established theoretically. According to the established theoretical relationship, the improvement of detection performance by fusion is further studied numerically. Furthermore, an optimization method is proposed to guide the design of the sub-detectors to achieve a better comprehensive performance. The effectiveness of this combined approach is validated by application to the detection of pedestrian, in which a support vector machine trained by the HOG feature of pedestrian is adopted as the classical detector and a comparatively simple transfer convolutional neural network (CNN) based on AlexNet structure acts as the deep learning detector. Several comparative tests with the classical and CNN detectors on the training dataset and other totally different dataset have been conducted to show the advantage of the combined one in ensuring detection performance with simpler network and adaptability to new application conditions.

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Robust Distributed Consensus Control of Uncertain Multiagents Interacted by Eigenvalue-Bounded Topologies

Cited by 38 publications

References 15 publications

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

Evolution test by improved genetic algorithm with application to performance limit evaluation of automatic parallel parking system

A Combined Object Detection Method With Application to Pedestrian Detection

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