Collision avoidance road test for COLREGS-constrained autonomous vehicles

Woerner, Kyle; Benjamin, Michael R.; Novitzky, Michael; Leonard, John J.

doi:10.1109/oceans.2016.7761413

Cited by 18 publications

(12 citation statements)

References 4 publications

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“…However, the methods have previously been combined to achieve data-driven synthesis of requirements. 13 There also exist previous works in automatic evaluation of maritime Collision Avoidance (CA) systems for autonomous ships, including Woerner, 14 which uses tailored penalty functions. This approach is further developed by Pedersen et al 3 Stankiewicz and Mullins 15 propose a different approach by running a large number of simulations and mapping decision boundaries using clustering methods.…”

Section: Introductionmentioning

confidence: 99%

Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic

Torben

Glomsrud

Pedersen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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A methodology for automatic simulation-based testing of control systems for autonomous vessels is proposed. The work is motivated by the need for increased test coverage and formalism in the verification efforts. It aims to achieve this by formulating requirements in the formal logic Signal Temporal Logic (STL). This enables automatic evaluation of simulations against requirements using the STL robustness metric, resulting in a robustness score for requirements satisfaction. Furthermore, the proposed method uses a Gaussian Process (GP) model for estimating robustness scores including levels of uncertainty for untested cases. The GP model is updated by running simulations and observing the resulting robustness, and its estimates are used to automatically guide the test case selection toward cases with low robustness or high uncertainty. The main scientific contribution is the development of an automatic testing method which incrementally runs new simulations until the entire parameter space of the case is covered to the desired confidence level, or until a case which falsifies the requirement is identified. The methodology is demonstrated through a case study, where the test object is a Collision Avoidance (CA) system for a small high-speed vessel. STL requirements for safety distance, mission compliance, and COLREG compliance are developed. The proposed method shows promise, by both achieving verification in feasible time and identifying falsifying behaviors which would be difficult to detect manually or using brute-force methods. An additional contribution of this work is a formalization of COLREG using temporal logic, which appears to be an interesting direction for future work.

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Section: Introductionmentioning

confidence: 99%

Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic

Torben

Glomsrud

Pedersen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…There is a scenario set for collision avoidance tests. Woerner et al proposed the scenario set [32]. This scenario has six situations of ship encounters.…”

Section: Scenariomentioning

confidence: 99%

Automatic ship collision avoidance using deep reinforcement learning with LSTM in continuous action spaces

2020

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This paper presents an automatic collision avoidance algorithm for ships using a deep reinforcement learning (DRL) in continuous action spaces. Obstacle zone by target (OZT) is used to compute an area where a collision will happen in the future based on dynamic information of ships. Agents of DRL detects the approach of multiple ships using a virtual sensor called the grid sensor. Agents learned collision avoidance maneuvering through Imazu problem, which is a scenario set of ship encounter situations. In this study, we propose a new approach for collision avoidance with a longer safe passing distance using DRL. We develop a novel method named inside OZT that expands OZT to improve the consistency of learning. We redesign the network using the long short-term memory (LSTM) cell and carried out training in continuous action spaces to train a model with longer safe distance than the previous study. The bow cross range in collision detection proposed in this paper is effective to COLREGs-compliant collision avoidance. The trained model has passed all scenarios of Imazu problem. The model is also validated by a test scenario which includes more ships than each scenario of Imazu problem.

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“…As compliance with COLREGS encompasses many different aspects, the focus in this study is about general vessel encounters, including conduct of vessels in sight of one another and operating under rules 13–17 for power-driven vessels. Categories of scope for COLREGS can be found in Woerner et al (2016) and Woerner (2016). Here, three primary encounter situations are considered: crossing, head-on, and overtaking.…”

Section: Path Planning Problemmentioning

confidence: 99%

COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields

2018

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This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.

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Collision avoidance road test for COLREGS-constrained autonomous vehicles

Cited by 18 publications

References 4 publications

Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic

Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic

Automatic ship collision avoidance using deep reinforcement learning with LSTM in continuous action spaces

COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields

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