Spatiotemporal Local-Remote Senor Fusion (ST-LRSF) for Cooperative Vehicle Positioning

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.

Section: Related Workmentioning

confidence: 99%

A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Godoy

Jiménez

Artuñedo

et al. 2021

“…As a part of the vehicular network, vehicle positioning plays an important role in intelligent traffic management systems, vehicle detection, autonomous driving, intelligent parking, and so on [ 1 ]. Traditional vehicle positioning usually adopts global navigation satellite system (GNSS) represented by global positioning system (GPS), GLONASS, Beidou navigation system (BDS), and Galileo [ 2 , 3 , 4 , 5 , 6 , 7 ]. However, in urban canyon environments, GNSS signals are often blocked by high-rise buildings and perform with low capability in terms of positioning [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments

Deng

Zheng

Wang

et al. 2020

Vehicle positioning with 5G can effectively compensate for the lack of vehicle positioning based on GNSS (Global Navigation Satellite System) in urban canyons. However, there is also a large ranging error in the non-line of sight (NLOS) propagation of 5G. Aiming to solve this problem, we consider a new time delay estimation algorithm called non-line of sight cancellation multiple signal classification (NC-MUSIC). This algorithm uses cross-correlation to identify and cancel the NLOS signal. Then, an unsupervised multipath estimation method is used to estimate the number of multipaths and extract the noise subspace. The MUSIC spectral function can be calculated by the noise subspace. Finally, the time delay of the direct path is estimated by searching the peak of MUSIC spectral function. This paper adopts the 5G channel model developed by 3GPP TR38.901 for simulation experiments. The experiment results demonstrated that the proposed algorithm has obvious advantages in terms of NLOS propagation for urban canyon environments. It provided a high-precision time delay estimation algorithm for observed time difference of arrival (OTDOA), joint angle of arrival (AOA) ranging, and other positioning methods in the 5G vehicle positioning method, which can effectively improve the positioning accuracy of 5G vehicle positioning in urban canyon environments.

“…Introducing new sources of information is an effective way of improving vehicular self-positioning. V2X communication, which has drawn increasing interest in recent years, renders information easily accessible to the vehicles connected [12,13,14]. The V2X-based (or cooperative) method aids in improving vehicular localization capability by employing the position information of other vehicles and relative measurements from their on-board sensors [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles

Xiao

Yang

Wen

et al. 2019

Future intelligent transport systems depend on the accurate positioning of multipletargets in the road scene, including vehicles and all other moving or static elements. The existingself-positioning capability of individual vehicles remains insufficient. Also, bottlenecks indeveloping on-board perception systems stymie further improvements in the precision and integrityof positioning targets. Vehicle-to-everything (V2X) communication, which is fast becoming astandard component of intelligent and connected vehicles, renders new sources of informationsuch as dynamically updated high-definition (HD) maps accessible. In this paper, we propose aunified theoretical framework for multiple-target positioning by fusing multi-source heterogeneousinformation from the on-board sensors and V2X technology of vehicles. Numerical and theoreticalstudies are conducted to evaluate the performance of the framework proposed. With a low-costglobal navigation satellite system (GNSS) coupled with an initial navigation system (INS), on-boardsensors, and a normally equipped HD map, the precision of multiple-target positioning attainedcan meet the requirements of high-level automated vehicles. Meanwhile, the integrity of targetsensing is significantly improved by the sharing of sensor information and exploitation of mapdata. Furthermore, our framework is more adaptable to traffic scenarios when compared withstate-of-the-art techniques.