Interval Analysis Technique for Versatile and Parallel Multi-Agent Collision Detection and Avoidance

Vyas, Pranjal; Vachhani, Leena; Sridharan, K.

doi:10.1007/s10846-019-01091-1

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…When each agent's velocity is adjusted in parallel, the velocities in the non-intersecting distance inevitably avoid colliding. This agreement protocol is used in competing speeds in a common interval 55 . As a navigation query, collision detection and avoidance in agents 56 or multi-agent [57][58][59] scenarios has also been addressed.…”

Section: Chain Collision or Mvcs Descriptionmentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

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Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

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Section: Chain Collision or Mvcs Descriptionmentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This requires an internal bus system, and a protocol to distribute this bus to the blocks. Wishbone B4 [38] is a bus system used in connecting IP blocks (Intellectual Properties), especially the open-source IP blocks. Before Wishbone B4 was created, open-source IPs used heterogeneous bus standards, making it difficult to integrate these IPs into the same system.…”

Section: Positioning Decoder Blockmentioning

confidence: 99%

Profile generation using the filtering technique for the fast motion and smooth performance in the hardware level of autonomous system

Lam,

Ngo,

Ngo

2023

IET Science Measure & Tech

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This study addresses the challenge of motion control in autonomousvehicles through the introduction of a novel profile generation design. Specifically,autonomous vehicles often contend with uncertain factors and physicallimitations within their operational environments, such as abrupt changes inacceleration or intricate parametric motion profiles. To tackle this problem, afiltering technique for motion profile generation is proposed, leveraging ahardware programming language as its foundation. The investigation begins byanalyzing the specific structure of the mobile platform, which includes twoactive side wheels, two passive rear wheels, a high load capacity, and adifferential drive mode. Building upon this theoretical basis, the proposedfiltering technique is introduced to attain smooth motion profiles and optimizetiming. Furthermore, the study suggests the use of FPGA (Field ProgrammableGate Array) acceleration to expedite these computations for swift motionprocessing. To validate the efficacy of the proposed method, both the mobilevehicle and the load are simulated as a one‐axis linear ball‐screw system withan aluminum ruler. The experimental results unequivocally demonstrate theeffectiveness, feasibility, and applicability of the proposed approach across avariety of industrial solutions.

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“…When all of the agent velocities are chosen at the same time, conflicts will almost definitely be avoided due to velocities at nonintersecting distances. In a common interval, the selection of competing speeds involves the agreement protocol [38]. Collision detection and avoidance in agents [39] or multi-agent scenarios [40] have also been discussed as a navigation query.…”

Section: ) Chain Collision Avoidance Techniquesmentioning

confidence: 99%

Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles

et al. 2022

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Interval Analysis Technique for Versatile and Parallel Multi-Agent Collision Detection and Avoidance

Cited by 7 publications

References 43 publications

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Profile generation using the filtering technique for the fast motion and smooth performance in the hardware level of autonomous system

Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles

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