Performance Modelling of V2V based Collective Perceptions in Connected and Autonomous Vehicles

Huang, Hui; Fang, Wenqi; Li, Huiyun

doi:10.1109/lcn44214.2019.8990854

Cited by 14 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mentioned on-ramping scenario is a good example where the SAR may be more meaningful than the OAR, since the vehicle is interested in finding a suitable gap between the vehicles in the highway, and only knowing the position of some of the vehicles would not be sufficient. A SAR-like metric is made use of in [ 19 , 40 , 41 ].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…It is composed of all arising latencies, the major ones being the just introduced update period, the sensor update rate, processing times and further communication delays [ 7 ]. Several papers have made use of the DOR [ 7 , 12 , 30 , 31 , 40 , 41 , 42 ], the update rate or period [ 6 , 7 , 11 , 12 , 32 , 35 ], the distance covered by the object between updates [ 6 , 7 , 32 ], and the AOI [ 6 , 7 ].…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Collective Perception: A Safety Perspective

Schiegg

Llátser

Bischoff³

et al. 2020

Sensors

View full text Add to dashboard Cite

Vehicle-to-everything (V2X) communication is seen as one of the main enabling technologies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to “see through the eyes of others” by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redundancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic participants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only contributes to the accuracy and integrity of the vehicles’ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a “risk of serious traffic accidents” to a “residual hypothetical risk of collisions without minor injuries” for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing worldwide standardization efforts of the collective perception service.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Collective Perception: A Safety Perspective

Schiegg

Llátser

Bischoff³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…It is provided in [ 26 ] an in-depth study on the impact of different CPM generation rules from the perspectives of V2X communication performance and perception capabilities in low and high density traffic scenarios. The authors of [ 27 ] raise the concern of redundant data sharing in V2V based CP with the increase of CAV penetration rate. To tackle the redundant transmission issue, a probabilistic data selection approach is presented in [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

“…The work presented in [ 13 ] evaluates EPM for obstacle avoidance of two manually driven CAVs, showing that the CP helps gain extra reaction time for the vehicles to avoid obstacles. It is analysed in [ 27 ] the performance gain in extending horizon of CAVs by leveraging V2V based CP. The work in [ 33 , 34 ] analytically evaluates the enhancement of environmental perception for CVs at different CP service penetration rates and with different traffic densities.…”

Section: Related Workmentioning

confidence: 99%

“…A significant proportion of the existing work conducts the analyses of V2X communication and CP in simulated environments. For instance, the work in [ 27 , 28 ] is carried out in an open source microscopic road traffic simulation package SUMO (Simulation of Urban Mobility) [ 48 ]. Another commonly used network and mobility simulator is Veins (Vehicles in Network Simulation) [ 49 , 50 ], which integrates SUMO and a discrete-event simulator OMNeT++ (Open Modular Network Testbed in C++) [ 51 ] for modelling realistic communication patterns.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations

Shan

Narula

Wong³

et al. 2020

Sensors

View full text Add to dashboard Cite

Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision-making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.

show abstract