A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Godoy, Jorge; Jiménez, Víctor M.; Artuñedo, Antonio; Villagrá, Jorge

doi:10.3390/s21030744

Cited by 24 publications

(18 citation statements)

References 38 publications

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“…The perception and motion prediction module uses exteroceptive sensors to estimate the status of traffic agents present on the scene and an occupancy grid

of the ego-vehicle surroundings [ 22 ]. It also processes GPS and proprioceptive sensors to generate a reliable state estimation (

) of the ego-vehicle.…”

Section: Motion Planning Architecturementioning

confidence: 99%

Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios

Lee

Artuñedo

Godoy

et al. 2021

Sensors

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Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories and selects the best of them according to a merit function that combines longitudinal comfort, lateral comfort, safety and utility criteria. The system was tested in urban scenarios on simulated and real environments, and the results show that different driving styles can be achieved according to the priorities set in the merit function, always meeting safety and comfort parameters imposed by design.

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“…The perception and motion prediction module uses exteroceptive sensors to estimate the status of traffic agents present on the scene and an occupancy grid

of the ego-vehicle surroundings [ 22 ]. It also processes GPS and proprioceptive sensors to generate a reliable state estimation (

) of the ego-vehicle.…”

Section: Motion Planning Architecturementioning

confidence: 99%

Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios

Lee

Artuñedo

Godoy

et al. 2021

Sensors

Self Cite

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“…• Measurements Z n t : represents the real measurements of the physical state of the vehicle, extracted directly from exteroceptive sensors of the ego-vehicle or via V2X communications [29].…”

Section: Figure 9: Bayesian Networkmentioning

confidence: 99%

Interaction-Aware Intention Estimation at Roundabouts

Trentin¹,

Artuñedo

Godoy

et al. 2021

IEEE Access

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Roundabouts have many benefits when compared with traditional signal-controlled intersections: improve safety, reduce delay, improve traffic flow, are less expensive and occupy less area. The behavior of traffic participants is full of uncertainties in the real world. An automated system that relies only on its perception is unable to safely enter the roundabout until a large gap occurs or the vehicle approaching has actually left the roundabout or passed the conflict area. In order to improve the driving quality, autonomous vehicles should be able to infer the correct intention at roundabout as early as possible. In this work, a method to classify the intentions of the surrounding vehicles at unsignalized roundabouts is proposed. For each vehicle at the scene, a Dynamic Bayesian Network is instantiated and the intentions are inferred using a particle filter.

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“…The tracking quality of locally perceived objects will not be improved by the perception data from other ITS-Ss, which would otherwise be achieved in joint fusion schemes. There are also occupancy-grid based data fusion methods for CP, such as in [6], [7]. This kind of fusion framework is considered computationally expensive, where pixel-wise iterative fusion is required.…”

Section: Introductionmentioning

confidence: 99%

A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception

Shan¹,

Narula²,

Wong³

et al. 2022

Preprint

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The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technology has enormous potential for improving efficiency and safety of road transportation. Recent advances in V2X communication primarily address the definition of V2X messages and data dissemination amongst ITS stations (ITS-Ss) in a traffic environment. Yet, a largely unsolved problem is how a connected vehicle (CV) can efficiently and consistently fuse its local perception information with the data received from other ITS-Ss. In this paper, we present a novel data fusion framework to fuse the local and V2X perception data for CP that considers the presence of cross-correlation. The proposed approach is validated through comprehensive results obtained from numerical simulation, CARLA simulation, and real-world experimentation that incorporates V2X-enabled intelligent platforms. The realworld experiment includes a CV, a connected and automated vehicle (CAV), and an intelligent roadside unit (IRSU) retrofitted with vision and lidar sensors. We also demonstrate how the fused CP information can improve the awareness of vulnerable road users (VRU) for CV/CAV, and how this information can be considered in path planning/decision making within the CAV to facilitate safe interactions.

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A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Cited by 24 publications

References 38 publications

Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios

Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios

Interaction-Aware Intention Estimation at Roundabouts

A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception

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