Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds

Chen, Qi; Tang, Sihai; Yang, Qing; Fu, Song

doi:10.48550/arxiv.1905.05265

Cited by 6 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Fig. 2, we provide several driving scenarios that are recorded in the T&J dataset [5]. These are the LiDAR data of an autonomous vehicle, with each key frame being a time step forward from the previous scanning position as the vehicles is moving along a straight path.…”

Section: B Detection Failures On Autonomous Vehiclesmentioning

confidence: 99%

“…Object detection failures and visual obstructions are both core difficulties that all autonomous vehicle must face. Techniques such as cooperative perception (COOPER) [5] and others address this problem from a fundamental level through fusion. While detection results are improved, the wireless bandwidth available for V2X communications is too limited to support huge amount of data transmission among vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…Currently-known fusion methods for connected and autonomous vehicles are categorized into three types: lowlevel, middle-level and high-level fusions. Low-level fusion is also called raw data fusion in which the original sensing data produced by vehicles are transmitted and shared among vehicles [17], [5]. While middle-level fusion methods make use of the extracted features from raw data to conduct fusion [14], high-level fusion mainly combines the sensing results processed by individual vehicles [1].…”

Section: Related Workmentioning

confidence: 99%

“…With the current works detailing the ground work, we know that communication in between vehicles plays an important limiting role based on the type of fusion methods being utilized. Taking COOPER [5] for example, while this method improves detection by merging point cloud data, it is limited by the narrow bandwidth available in vehicular networks. Not only does using higher quality sensors increase the amount of data that gets generated, using higher quality data also posses the risk that the data being generated will be too big to be transferred efficiently.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Low-Latency High-Level Data Sharing for Connected and Autonomous Vehicular Networks

Chen

Tang

Hochstetler

et al. 2019

2019 IEEE International Conference on Industrial Internet (ICII)

Self Cite

View full text Add to dashboard Cite

Autonomous vehicles can combine their own data with that of other vehicles to enhance their perceptive ability, and thus improve detection accuracy and driving safety. Data sharing among autonomous vehicles, however, is a challenging problem due to the sheer volume of data generated by various types of sensors on the vehicles. In this paper, we propose a low-latency, high-level (L3) data sharing protocol for connected and autonomous vehicular networks. Based on the L3 protocol, sensing results generated by individual vehicles will be broadcasted simultaneously within a limited sensing zone. The L3 protocol reduces the networking latency by taking advantage of the capture effect of the wireless transmissions occurred among vehicles. With the proposed design principles, we implement and test the L3 protocol in a simulated environment. Simulation results demonstrate that the L3 protocol is able to achieve reliable and fast data sharing among autonomous vehicles.

show abstract

Section: B Detection Failures On Autonomous Vehiclesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Low-Latency High-Level Data Sharing for Connected and Autonomous Vehicular Networks

Chen

Tang

Hochstetler

et al. 2019

2019 IEEE International Conference on Industrial Internet (ICII)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, trust was introduced in the concept of social cloud [20,21,22,23,24,25,26,27,28]. Trust is also introduced in cyber-physical and edge computing systems, e.g., wireless sensor networks [29,30,31,32,33,34,35,36,37,38] and vehicular networks [39,40,41,42]. Another important domain in which trust analysis is widely applied is Sybil defense and spam detection [43,3,44,16,45].…”

Section: Related Workmentioning

confidence: 99%

A Probabilistic Graph Model for Trust Opinion Estimation in Online Social Networks

Liu,

Yang

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Trust assessment plays a key role in many online applications, such as online money lending, product reviewing and active friending. Trust models usually employ a group of parameters to represent the trust relation between a trustor-trustee pair. These parameters are originated from the trustors bias and opinion on the trustee. Naturally, these parameters can be regarded as a vector. To address this problem, we propose a framework to accurately convert the single values to the parameters needed by 3VSL. The framework firstly employs a probabilistic graph model (PGM) to derive the trustors opinion and bias to his rating on the trustee.

show abstract

Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds

Chen

Tang

Yang

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

Self Cite

275

242

View full text Add to dashboard Cite

Autonomous vehicles are heavily reliant upon their sensors to perfect the perception of surrounding environments, however, with the current state of technology, the data which a vehicle uses is confined to that from its own sensors. Data sharing between vehicles and/or edge servers is limited by the available network bandwidth and the stringent real-time constraints of autonomous driving applications. To address these issues, we propose a point cloud feature based cooperative perception framework (F-Cooper) for connected autonomous vehicles to achieve a better object detection precision. Not only will feature based data be sufficient for the training process, we also use the features' intrinsically small size to achieve real-time edge computing, without running the risk of congesting the network. Our experiment results show that by fusing features, we are able to achieve a better object detection result, around 10% improvement for detection within 20 meters and 30% for further distances, as well as achieve faster edge computing with a low communication delay, requiring 71 milliseconds in certain feature selections. To the best of our knowledge, we are the first to introduce feature-level data fusion to connected autonomous vehicles for the purpose of enhancing object detection and making realtime edge computing on inter-vehicle data feasible for autonomous vehicles.

show abstract

Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds

Cited by 6 publications

References 18 publications

Low-Latency High-Level Data Sharing for Connected and Autonomous Vehicular Networks

Low-Latency High-Level Data Sharing for Connected and Autonomous Vehicular Networks

A Probabilistic Graph Model for Trust Opinion Estimation in Online Social Networks

Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds

Contact Info

Product

Resources

About