Survey of connected automated vehicle perception mode: from autonomy to interaction

Shangguan, Wei; Du, Yu; Guo, Chai Lin; Dan, Liu; Shu, Wang Wei

doi:10.1049/iet-its.2018.5239

Cited by 26 publications

(11 citation statements)

References 58 publications

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“…I propose to use the introduced scenario control models if the perception module is assumed to be a black box. However, if there is detailed information about the investigated perception module, a model-based representation of the tested perception system is suggested [57]. Let us now describe the perception modules by the bounding points of their field of view (X FW 1 , Y FW 1 .…”

Section: ) Scenario Control Using a Detailed Sensor Modelmentioning

confidence: 99%

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Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Szalay

2021

IEEE Access

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Testing self-driving vehicles is still a new and immature process; the globally harmonised procedure expected much later. The resource-demanding nature of real-world tests makes it indispensable to develop and improve the efficiency of virtual environment based testing methods. Accordingly, a novel Xin-the-Loop framework is proposed to fully exploit the recent advances in info-communication technologies, vehicle automation, and testing and validation requirements. This methodology real-time connects physical and virtual testing with high correlation while completely blurs the sharp boundaries between them. Measurement results confirm the superior performance of the 5G communication link in providing a stable, real-time connection between the real world and its virtual representation. The live demonstration proved the presented concept at the newly constructed Hungarian proving ground for automated driving. The performed investigation also includes comprehensive benchmarking, focusing on the most up-to-date automotive testing frameworks. The analysis considers the methodologies and techniques applied by the most relevant actors in the automotive testing sector worldwide. Accordingly, the newly developed testing framework is evaluated and validated in light of the state-of-the-art methods used by the automotive industry.INDEX TERMS Automotive proving ground, automated vehicle control, autonomous vehicle, co-simulation, digital twin, mixed-reality testing, scenario-based testing, testing and validation, virtual and mixed reality, V-model, X-in-the-loop testing.

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Section: ) Scenario Control Using a Detailed Sensor Modelmentioning

confidence: 99%

“…1). It means in practice that some of the input signals can be perceived directly from the real environment [57], and other signals can be parallel simulated by the applied comprehensive software framework, connected to the lower layers of the perception and communication architecture.…”

Section: A Introduction Of the Modelmentioning

confidence: 99%

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Szalay

2021

IEEE Access

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“…Collaborative perception is a fundamental capability in collaborative robotics for robots and other agents including humans in a collaborative team to share information of the surrounding environment thus achieving shared situational awareness among the teammates. Collaborative perception has been widely applied in a variety of real-world applications including human-robot collaborative assembly [18,20], multi-robot search and rescue [1,45], and connected autonomous driving [19,49]. Correspondence identification is defined as a problem to identify the same objects observed by multiple agents in their own fields of view, which is considered an essential component to enable collaborative perception [14,17,43].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Deep Graph Matching for Correspondence Identification in Collaborative Perception

Gao¹,

Zhang²

2021

Robotics: Science and Systems XVII

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Correspondence identification is essential for multirobot collaborative perception, which aims to identify the same objects in order to ensure consistent references of the objects by a group of robots/agents in their own fields of view. Although recent deep learning methods have shown encouraging performance on correspondence identification, they suffer from two shortcomings, including the inability to address non-covisibility in collaborative perception that is caused by occlusion and limited fields of view of the agents, and the inability to quantify and reduce uncertainty to improve correspondence identification. To address both issues, we propose a novel uncertainty-aware deep graph matching method for correspondence identification in collaborative perception. Our new approach formulates correspondence identification as a deep graph matching problem, which identifies correspondences based upon graph representations that are constructed from the agents' observations. We introduce a novel deep graph matching network under the Bayesian framework to explicitly quantify uncertainty in the identified correspondences. In addition, we design a novel loss function that explicitly reduces correspondence uncertainty and perceptual non-covisibility during learning. We evaluate our approach in the robotics applications of collaborative assembly and multi-robot coordination using high-fidelity simulations and physical robots. Experiments have shown that, through addressing both uncertainty and non-covisibility, our approach achieves the state-of-the-art performance of correspondence identification.

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“…Another evidence of IVNCS complexity resides in the tight interaction between software (SW) functions and hardware (HW) components [6]. Especially for the intelligent vehicles, the IVNCS outgrowth is accentuated due to the arising attitudes to enhance the vehicle inter‐connectivity with its environment [7, 8]. The evasive integration of sensing tools brings additional complications.…”

Section: Introductionmentioning

confidence: 99%

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Lakhal

Nasri

Adouane

et al. 2020

IET intell. transp. syst

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The in‐vehicular networked control system is among the most critical embedded processes. The controller area network (CAN) has prevailed intra‐vehicle communication for decades. Meanwhile, requirements of future transportation systems are expected to emphasise the in‐vehicle communication complexity, which endangers the reliability/safety of the intelligent navigation. At first, this study reviews the recent solutions proposed to overcome the CAN expanding complexity. Challenges that tomorrow's intelligent vehicles may raise for CAN reliability are investigated. The comprehensive coverage of current research efforts to remove the impact of these challenges is presented. Further, the in‐vehicle system reliability of future automated vehicles is also related to the fault diagnosis performances. Hence, different classes of system‐level diagnosis strategies are compared relatively to the requirements of automotive embedded networks. Furthermore, to thoroughly cover CAN reliability engineering issues, focus is given to the automotive validation techniques. The hardware in the loop, real‐time analysis and computer‐aided‐design tools intervene in various phases along the in‐vehicular network life cycle. Parameters that stand behind the efficiency and accuracy of these techniques in validating the new generation of vehicles are analysed. The authors finally draw some deductive predictions about the future directions related to the reliability of the intelligent transportation system in‐vehicular communication.

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Survey of connected automated vehicle perception mode: from autonomy to interaction

Cited by 26 publications

References 58 publications

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Bayesian Deep Graph Matching for Correspondence Identification in Collaborative Perception

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

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