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Autonomous vehicles are equipped with multiple heterogeneous sensors and drive while processing data from each sensor in real time. Among the sensors, the global navigation satellite system (GNSS) is essential to the localization of the vehicle itself. However, if a GNSS-denied situation occurs while driving, the accident risk may be high due to the degradation of the vehicle positioning performance. This paper presents a cooperative positioning technique based on the lidar sensor and vehicle-to-everything (V2X) communication. The ego-vehicle continuously tracks surrounding vehicles and objects, and localizes itself using tracking information from the surroundings, especially in GNSS-denied situations. We present the effectiveness of the cooperative positioning technique by constructing a GNSS-denied case during autonomous driving. A numerical simulation using a driving simulator is included in the paper to evaluate and verify the proposed method in various scenarios.
As defined by the Society of Automotive Engineers (SAE) International in 2016, autonomous driving technology is subdivided into six levels, depending on the required conditions and functions [1]. Among them, Level 2 is the stage of a driving assistance system that simultaneously performs longitudinal and lateral control, and the vehicle is driven in a partially automated state with the human driver still having to concentrate on driving. Level 3 refers to a stage where, as with Level 2, the vehicle is operated in a partially automated state; however, driver intervention is much reduced. Level 4 or higher performs autonomous driving without driver intervention depending on a given constraint, such as weather conditions. A more advanced autonomous driving system must be implemented to achieve the step after Level 3, in which the driver intervention is gradually reduced.
The autonomous driving system works in the order of recognition, decision, and control; its smooth operation requires a processing system, an electronic control unit, and various sensors [2, 3]. In an autonomous driving system, recognition sensors consist of cameras, lidar, radar, and others to provide the relative position of the surrounding objects. Ego-vehicle positioning sensors can include a global navigation satellite system (GNSS) receiver and an inertial measurement unit (IMU) to provide location and status information. Among them, the GNSS sensor receives satellite signals at a low power on the ground, originally transmitted by satellites orbiting at an altitude of >20, 000 km. It processes signals to provide the user location, velocity, direction, and time information on a global scale [4]. However, GNSS-equipped vehicles may be affected by multipath or degraded signals and signal blockage due to the signal-receiving environment (i.e., GNSS-denied environment). Moreover, the failure or system malfunction of a GNSS sensor could lead to an unpredictable accident. Therefore, it is necessary to estimate the current position in any situation for safe driving.
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Nazaruddin et al. presented the ego-vehicle localization method, based on the IMU sensor in the GNSS disconnection situation, which may be caused by high dependence on the external environment or a low sampling rate. This technique predicts the next coordinates from the previous GNSS and current IMU sensor data. The localization uses an error-state Kalman filter (KF) with a residual long short-term memory model [5]. A similar approach to the GNSS outage using two complementary sensors, such as the IMU and odometer, was proposed in [6], where the odometer and GNSS measurements were exploited to correct IMU errors during GNSS availability. Then, the odometer was used to correct IMU errors during GNSS outages to improve the positioning accuracy while ensuring the continuity of the navigation solution.
Furthermore, various localization methods, such as the visual or laser simultaneous localization and mapping (SLAM) technique, have been proposed using perception sensors, such as cameras, radar, and lidar, cooperatively to achieve better navigational information [7]. Satellite or geographic information system images are used with visual odometry, even with more storage and computational requirements [8, 9]. These methods can simultaneously support two functions, such as visually surveilling the required geographical areas and nearby obstacles and estimating the navigational state (e.g., position, velocity, and attitude), especially in GNSS-denied environments.
However, these methods may have the drawback that the predetermined position information of surrounding objects is required for localization. To overcome this limitation, vehicle-to-everything (V2X) communication [10], where status information including the position of the surrounding objects can be exchanged, is ideally suited for sensor fusion. A radar-based ego-vehicle localization method using vehicle-to-vehicle (V2V) communication was proposed in [11]. In GNSS-denied situations, this method estimates the absolute coordinates of the ego-vehicle by combining the relative coordinates of the object vehicles obtained using radar with the absolute coordinates of the object vehicles received through V2V communication. Ma et al. presented a vehicle-to-infrastructure (V2I)-based vehicle localization algorithm, where a low-cost IMU-assisted single roadside unit and a GNSS receiver-based localization algorithm based on the least-squares method were used to reduce the deployment costs [12]. Perea-Strom et al. presented a fusion method of wheel odometry, IMU, GNSS, and lidar using the adaptive Monte Carlo localization algorithm, where a particle weighting model to integrate GNSS measurements was adopted to perform ego-vehicle localization in a 2D map [13].
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One way to improve the accuracy of localization algorithms from surrounding objects is to increase their confidence in the surrounding objects. To do this, it is necessary to apply a tracking algorithm to the surrounding objects. The KF-based tracking algorithm using 2D or 3D object information obtained through the camera and lidar is widely used for continuous object tracking [14]. In addition, a study on model-free tracking based on the support vector machine classifier learned with the histogram of oriented gradients feature was performed [15]. Research on the object-tracking algorithm incorporated with deep learning is still underway. For example, a convolutional neural network was used as a feature extractor, and more accurate tracking was performed through the adaptive hedge method [16]. Furthermore, the two-stage Siamese re-detection architecture and tracklet-based dynamic programming algorithm were combined for the re-detection and tracking of objects even after long occlusion [17].
This paper presents a localization method for the ego-vehicle in GNSS-denied environments, using lidar and V2X communication cooperatively. Sensor fusion and localization for surrounding vehicles and objects, and the ego-vehicle using the lidar sensor and V2X communication are presented. Multiobject tracking (MOT) using an extended KF (EKF) and a localization method using a particle filter (PF) are described. An autonomous driving simulator modeled by each sensor is used to verify this, and comparative verification is performed based on the truth and predicted trajectory obtained from the simulator.
The cooperative localization presented in this paper is a two-step approach. In the first step, the method estimates the coordinates of the surrounding vehicles and objects based on the lidar sensor and V2X communication data. In the second step, the method estimates the ego-vehicle coordinates based on the results of the first step when the GNSS signal is unavailable. Figure 1 is the block diagram of the cooperative localization process. It consists of data processing module, sensor fusion module, and localization module. The first two are for localizing surrounding vehicles and objects, and the latter is for the ego-vehicle. For simplicity, in this paper, only the surrounding vehicles are assumed to be used, but the same metric can be applied to the surrounding objects.
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In the data processing module, the lidar sensor recognizes surrounding vehicles and their localization relative to the ego-vehicle. Simultaneously, the status information for the surrounding vehicles is independently obtained through the V2X (in fact, V2V) communication. Then, surrounding vehicle information obtained from the data processing module is merged in the sensor fusion module to perform highly reliable MOT in real time. In the localization module, the ego-vehicle coordinates are estimated through the PF using the MOT results of the sensor fusion module.
The data processing module of cooperative localization processes the lidar sensor output and V2X communication messages independently. It detects the surrounding vehicles based on the point cloud data (PCD) of the lidar sensor and receives a basic safety message (BSM) of V2X communication to obtain the surrounding vehicle data [18]. To recognize the vehicle from the PCD, a series
One way to improve the accuracy of localization algorithms from surrounding objects is to increase their confidence in the surrounding objects. To do this, it is necessary to apply a tracking algorithm to the surrounding objects. The KF-based tracking algorithm using 2D or 3D object information obtained through the camera and lidar is widely used for continuous object tracking [14]. In addition, a study on model-free tracking based on the support vector machine classifier learned with the histogram of oriented gradients feature was performed [15]. Research on the object-tracking algorithm incorporated with deep learning is still underway. For example, a convolutional neural network was used as a feature extractor, and more accurate tracking was performed through the adaptive hedge method [16]. Furthermore, the two-stage Siamese re-detection architecture and tracklet-based dynamic programming algorithm were combined for the re-detection and tracking of objects even after long occlusion [17].
This paper presents a localization method for the ego-vehicle in GNSS-denied environments, using lidar and V2X communication cooperatively. Sensor fusion and localization for surrounding vehicles and objects, and the ego-vehicle using the lidar sensor and V2X communication are presented. Multiobject tracking (MOT) using an extended KF (EKF) and a localization method using a particle filter (PF) are described. An autonomous driving simulator modeled by each sensor is used to verify this, and comparative verification is performed based on the truth and predicted trajectory obtained from the simulator.
The cooperative localization presented in this paper is a two-step approach. In the first step, the method estimates the coordinates of the surrounding vehicles and objects based on the lidar sensor and V2X communication data. In the second step, the method estimates the ego-vehicle coordinates based on the results of the first step when the GNSS signal is unavailable. Figure 1 is the block diagram of the cooperative localization process. It consists of data processing module, sensor fusion module, and localization module. The first two are for localizing surrounding vehicles and objects, and the latter is for the ego-vehicle. For simplicity, in this paper, only the surrounding vehicles are assumed to be used, but the same metric can be applied to the surrounding objects.
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In the data processing module, the lidar sensor recognizes surrounding vehicles and their localization relative to the ego-vehicle. Simultaneously, the status information for the surrounding vehicles is independently obtained through the V2X (in fact, V2V) communication. Then, surrounding vehicle information obtained from the data processing module is merged in the sensor fusion module to perform highly reliable MOT in real time. In the localization module, the ego-vehicle coordinates are estimated through the PF using the MOT results of the sensor fusion module.
The data processing module of cooperative localization processes the lidar sensor output and V2X communication messages independently. It detects the surrounding vehicles based on the point cloud data (PCD) of the lidar sensor and receives a basic safety message (BSM) of V2X communication to obtain the surrounding vehicle data [18]. To recognize the vehicle from the PCD, a series
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