Autonomous Driving Decision Algorithm for Complex Multi-Vehicle Interactions: An Efficient Approach Based on Global Sorting and Local Gaming

Li, Daofei; Zhang, Jiajie; Liu, Guanming

doi:10.1109/tits.2023.3346048

Cited by 2 publications

(1 citation statement)

References 35 publications

(50 reference statements)

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“…The GJK algorithm reduces the complexity of convex geometry by Minkowski distance, which has been widely used in computer graphics [19]. The swept volume intersection (SVI) method is suitable for detecting collisions along continuous paths [20]. It improves accuracy by assessing the collision situation based on the overlap rate.…”

Section: Geometric Representationmentioning

confidence: 99%

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Zhang,

Tao,

Jiang

et al. 2024

Machines

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Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method.

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Section: Geometric Representationmentioning

confidence: 99%

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Zhang,

Tao,

Jiang

et al. 2024

Machines

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Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation

Almutairi,

Asmari,

Alqubaysi

et al. 2024

Machines

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Road safety through point-to-point interaction autonomous vehicles (AVs) assimilate different communication technologies for reliable and persistent information sharing. Vehicle interaction resilience and consistency require novel sharing knowledge for retaining driving and pedestrian safety. This article proposes a control optimiser interaction framework (COIF) for organising information transmission between the AV and interacting “Thing”. The framework relies on the neuro-batch learning algorithm to improve the consistency measure’s adaptability with the interacting “Things”. In the information-sharing process, the maximum extraction and utilisation are computed to track the AV with precise environmental knowledge. The interactions are batched with the type of traffic information obtained, such as population, accidents, objects, hindrances, etc. Throughout travel, the vehicle’s learning rate and the surrounding environment’s familiarity with it are classified. The learning neurons are connected to the information actuated and sensed by the AV to identify any unsafe vehicle activity in unknown or unidentified scenarios. Based on the risk and driving parameters, the safe and unsafe activity of the vehicles is categorised with a precise learning rate. Therefore, minor changes in vehicular decisions are monitored, and driving control is optimised accordingly to retain 7.93% of navigation assistance through a 9.76% high learning rate for different intervals.

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Autonomous Driving Decision Algorithm for Complex Multi-Vehicle Interactions: An Efficient Approach Based on Global Sorting and Local Gaming

Abstract: e-Prints posted on TechRxiv are preliminary reports that are not peer reviewed. They should not b...

Cited by 2 publications

References 35 publications

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation

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