Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Srikanthakumar, Sivaranjini; Chen, Wenhua

doi:10.1017/s0263574714000642

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…And since their specific code is not publicly available, it is not possible to build directly on their work; for our work, the code will be opensource (for code, see links provided in Section VI) and further research can thus be easily built on it. Srikanthakumar et al in [25] developed an automatic approach to finding worst cases for moving obstacle avoidance algorithms based on simulations and optimization techniques (one of which is a GA). However, their optimization problem has only one objectiveto find a worst case situation that causes a collision.…”

Section: Fig 1 Overview Of the Testing Methods That Combines Agent-bamentioning

confidence: 99%

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

Zou

Alexander

McDermid

2016

Journal of Aerospace Information Systems

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We present a new approach to testing multi-UAV conflict resolution algorithms. We have formulated the problem as a multi-objective search problem, with two objectives: finding air traffic encounters that (1) are able to reveal faults in conflict resolution algorithms, and (2) are likely to happen in the real world. Our method uses agent-based simulation and multiobjective search to automatically find encounters satisfying these objectives. It describes pairwise encounters in 3D space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several pairwise encounters. The consequences of the encounters, given the conflict resolution algorithm, are explored using a fast-time agent-based simulator. To find encounters meeting the two objectives, we use a genetic algorithm approach. We have applied our method to test ORCA-3D, a widely-cited open source multi-UAV conflict resolution algorithm, and compared our method's performance against a plausible random testing approach. Our results show that our method can find the required encounters more efficiently than the random search. The identified safety incidents are then the starting points for understanding limitations of the conflict resolution algorithm.

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Section: Fig 1 Overview Of the Testing Methods That Combines Agent-bamentioning

confidence: 99%

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

Zou

Alexander

McDermid

2016

Journal of Aerospace Information Systems

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“…[26]- [30]. From existing and rapidly evolving types of advanced driver assistance systems (ADAS) to the high-level automation intelligent unmanned driving vehicle [31]- [33], the goal of fully intelligent transportation technology is drawing research attention using multi-sensor information fusion technology [34]- [38]. This is different to Front Collision Warning which only warns the potential collision risks [39]- [42].…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Vehicle Longitudinal Trajectory Collision Avoidance Strategy Using AEBS With Vehicle-Infrastructure Communication

Zhang

et al. 2022

IEEE Trans. Veh. Technol.

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Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This paper investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication capability in addition to radar for longitudinal vehicle control. High-level controller is designed to ensure safety of multi-cars and optimize Manuscript

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“…Results show that the algorithm adapts to the characteristics of high-speed USVs and can guide high-speed USVs (≥ 20 ≥ 20 math container loading MathJax knots) in realising safe navigation in real marine environments. But this method has the problem of low obstacle avoidance; according to Srikanthakumar and Chen (2015), the paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. It is demonstrated that a local nonlinear optimisation method may not be adequate, and global optimisation techniques are necessary to provide reliable worst-case analysis.…”

Section: Introductionmentioning

confidence: 99%

Visual automatic obstacle avoidance technology research in unmanned vehicles

Liu

Tan

et al. 2019

IJRIS

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In view of the problems of low obstacle avoidance and low efficiency in traditional unmanned vehicle in automatic obstacle avoidance, multi-feature fusion automatic obstacle avoidance method in unmanned vehicle is proposed. Optimise unmanned vehicle obstacle avoidance objective function, measure target obstacle distance, and calculate the braking distance. Based on this, unmanned vehicle dynamics model is established and the obstacle is located and determined. Multi-feature fusion design of unmanned vehicle visual automatic obstruction steps is made and finally the process of obstacle avoidance is analysed. The experimental results show that the use of improved visual automatic obstacle avoidance technology has certain advantages in target obstacle positioning and obstacle avoidance accuracy, which are superior to those of traditional obstacle avoidance technology.

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Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Cited by 7 publications

References 15 publications

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

A Multi-Vehicle Longitudinal Trajectory Collision Avoidance Strategy Using AEBS With Vehicle-Infrastructure Communication

Visual automatic obstacle avoidance technology research in unmanned vehicles

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