GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Lindenmaier, László; Aradi, Szilárd; Bécsi, Tamás; Törő, Olivér; Gáspár, Péter

doi:10.1109/tvt.2022.3171040

Cited by 8 publications

(5 citation statements)

References 48 publications

(57 reference statements)

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“…Moreover, a large number of computational resources can be consumed in complex scenarios with background noise, interference, and measurements. Therefore, we cite the method introduced by Lindenmaier [ 30 ] to prune the Gaussian components, and the accurate ICVs data states at the intersection were obtained. The pseudocode of GM-PHD algorithm is shown in Table 1 .…”

Section: Real-time Trajectory Prediction Methods For Intelligent Conn...mentioning

confidence: 99%

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Wang

Liu

et al. 2023

Sensors

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Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety.

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Section: Real-time Trajectory Prediction Methods For Intelligent Conn...mentioning

confidence: 99%

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Wang

Liu

et al. 2023

Sensors

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“…Likewise, ref. [ 19 ] differentiated the detection probability depending on the object being inside or outside the FoV, and did not consider the distance dependency.…”

Section: Proposed Multi-sensor Multi-object Tracking Approachmentioning

confidence: 99%

“…This paper shows that the application of sensor-specific models for these parameters can solve multiple problems of GM-PHD filters in real-world applications. In [ 19 ], this was addressed by differentiating the detection probability between inside and outside the FoV [ 16 ]. In addition [ 20 ], introduced occlusion models for those areas where tracks are not detectable.…”

Section: Introductionmentioning

confidence: 99%

Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter

Bader,

Schwieger

2024

Sensors

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Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA(2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception.

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“…The predicted density presented in Equation ( 13) is a compact form, but in Equation (20), it requires the summation of all supersets of L, and consequently, is more difficult to compute. To this end, [26] provided an equivalent version as follows:…”

Section: δ-Glmb Prediction For Nonlinear Gaussian Modelmentioning

confidence: 99%

“…Because of the multi-target Bayesian filter's numerical complexity, some alternative approaches have been proposed, including the probability hypothesis density (PHD) [19][20][21], cardinality PHD (CPHD) [22,23], and multi-Bernoulli filter [24,25] methods. Nevertheless, these methods do not function as multi-target trackers since they lack the ability to track target trajectories and, consequently, cannot discern the identity information of the targets.…”

Section: Introductionmentioning

confidence: 99%

Generalized Labeled Multi-Bernoulli Filter-Based Passive Localization and Tracking of Radiation Sources Carried by Unmanned Aerial Vehicles

Zhao,

Gui,

Dong

2024

Drones

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This paper discusses a key technique for passive localization and tracking of radiation sources, which obtains the motion trajectory of radiation sources carried by unmanned aerial vehicles (UAVs) by continuously or periodically localizing it without the active participation of the radiation sources. However, the existing methods have some limitations in complex signal environments and non-stationary wireless propagation that impact the accuracy of localization and tracking. To address these challenges, this paper extends the δ-generalized labeled multi-Bernoulli (GLMB) filter to the scenario of passive localization and tracking based on the random finite-set (RFS) framework and provides the extended Kalman filter (EKF) and unscented Kalman filter (UKF) implementations of the δ-GLMB filter, which fully take into account the nonlinear motion of the radiation source. By modeling the “obstacle scenario” and the influence of external factors (e.g., weather, terrain), our proposed GLMB filter can accurately track the target and capture its motion trajectory. Simulation results verify the effectiveness of the GLMB filter in target identification and state tracking.

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GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Cited by 8 publications

References 48 publications

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter

Generalized Labeled Multi-Bernoulli Filter-Based Passive Localization and Tracking of Radiation Sources Carried by Unmanned Aerial Vehicles

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