Object-level Perception Sharing Among Connected Vehicles

Ambrosin, Moreno; Álvarez, Ignacio; Buerkle, Cornelius; Yang, Lixin; Oboril, Fabian; Sastry, Manoj; Sivanesan, Kathiravetpillai

doi:10.1109/itsc.2019.8916837

Cited by 21 publications

(10 citation statements)

References 28 publications

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“…Different techniques have been proposed to properly fuse the heterogeneous and decentralized data that is available. A multi-target Kalman Filter is used together with classical association techniques in [ 18 ] to manage object-level sharing among vehicles. Iterative Closest Point (ICP) was used in [ 19 ] to minimize position and orientation offsets between object lists from different vehicles.…”

Section: Related Workmentioning

confidence: 99%

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Godoy

Jiménez

Artuñedo

et al. 2021

Sensors

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Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupancy from one step to the next, allowing for object tracking. The proposed framework was validated on a set of experiments using real vehicles and infrastructure sensors for sensing static and dynamic objects. Results showed a good performance even under important uncertainties and delays, hence validating the viability of the proposed framework for Collective Perception.

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Section: Related Workmentioning

confidence: 99%

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Godoy

Jiménez

Artuñedo

et al. 2021

Sensors

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“…Other approaches, e.g. [24], [25], as in this work, use collective perception, e.g., to resolve occlusions. To be scalable with respect to the data load on the network, these approaches share preprocessed object lists rather than raw data.…”

Section: A Related Workmentioning

confidence: 99%

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

Müller¹,

Strohbeck²,

Herrmann³

et al. 2021

Preprint

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Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address this challenge with a sampling-based optimization approach. For this, we formulate an optimal control problem that optimizes for low risk and high passenger comfort. The risk is calculated on the basis of the perception information and the respective uncertainty using a risk model. The risk model combines set-based methods and probabilistic approaches. Thus, the approach provides safety guarantees in a probabilistic sense, while for a vanishing risk, the formal safety guarantees of the setbased methods are inherited. By exploring all available behavior options, our approach solves decision making and longitudinal trajectory planning in one step. The available behavior options are provided by a formal representation of the situation context, which is also used to reduce calculation efforts. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track fusion, the information is used in parallel. The motion planning scheme is validated through real-world experiments.

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“…Relevant research regarding cooperative perception includes the localization of communicating vehicles and sensor data fusion for detecting other traffic participants. The latter can be broadly subdivided into object-level and map-level fusion [7]. The cooperative fusion of raw sensor data [8] is an apparent extension, but not the scope of previous work due to the challenge of transmitting the required data in real time.…”

Section: A Related Workmentioning

confidence: 99%

“…Fusing object lists is shown in [7], where sensor information transmitted from surrounding vehicles is treated as virtual sensor data supporting the global object tracker for each vehicle. To handle unknown correlations between object tracks perceived by different vehicles, covariance intersection filters [9] are applied.…”

Section: A Related Workmentioning

confidence: 99%

Cooperative Raw Sensor Data Fusion for Ground Truth Generation in Autonomous Driving

Spiegel

Althoff

2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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Ground truth data plays an important role in validating perception algorithms and in developing data-driven models. Yet, generating ground truth data is a challenging process, often requiring tedious manual work. Thus, we present a post-processing approach to automatically generate ground truth data from environment sensors. In contrast to existing approaches, we incorporate raw sensor data from multiple vehicles. As a result, our cooperative fusion approach overcomes drawbacks of occlusions and decreasing sensor resolution with distance. To improve the alignment precision for raw sensor data fusion, we include mutual detections and match the jointly-observed static environment to support differential global positioning system localization. We further provide a new registration algorithm, where all point clouds are moved simultaneously, while restricting the transformation parameters to increase the robustness against misalignments. The benefits of our raw sensor data fusion approach are demonstrated with real lidar data from two test vehicles in different scenarios.

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Object-level Perception Sharing Among Connected Vehicles

Cited by 21 publications

References 28 publications

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

Cooperative Raw Sensor Data Fusion for Ground Truth Generation in Autonomous Driving

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