A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application

Vivacqua, Rafael Peixoto Derenzi; Vassallo, Raquel Frizera; Martins, Felipe Nascimento

doi:10.3390/s17102359

Cited by 51 publications

(35 citation statements)

References 35 publications

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“…Map constraints in many localization methods are rather incomplete. As an example, in [36,39], lane lines can only provide lateral constraints, which is of very limited usability for IVs. In addition, the accuracy of the vehicle localization based solely on map matching methods are prone to sparse map features.…”

Section: Integration Of Frame-to-frame Constraintsmentioning

confidence: 99%

“…Besides fusing the absolute localization information such as GNSS, it can also exploit the inter-frame motion information for smoothing. For example, IMU, vehicle dynamic constraints, and wheel odometry have all been integrated in the localization system as map-matching supplements [3,39]. These additional inter-frame constraints can be easily incorporated into the prediction step in the filtering framework.…”

Section: Integration Of Frame-to-frame Constraintsmentioning

confidence: 99%

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Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Xiao

Yang

Wen

et al. 2020

Sensors

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Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.

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Section: Integration Of Frame-to-frame Constraintsmentioning

confidence: 99%

Section: Integration Of Frame-to-frame Constraintsmentioning

confidence: 99%

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Xiao

Yang

Wen

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Surrounding these developing technologies, the theories and mechanisms of vehicular smart perception have been studied recently [17][18][19][20][21][22][23][24][25]. With the ability of precise perception, the foundation of autonomous driving has been getting better and better.…”

Section: Autonomous Driving Studymentioning

confidence: 99%

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

et al. 2018

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With the coming of intelligent vehicles and vehicular communication, Intelligent Transportation Systems (ITS) of connected vehicles are emerging and now evolving to Cooperative-ITS (C-ITS), as service platforms for smart cities. Considering new service properties, the autonomous cooperation of such vehicles has exhibited novel QoS features that imply new requirements: guaranteeing the traffic efficiency of any emergent vehicle while trying to promote the throughput at an intersection. So, after analyzing the classic reservation-based cooperation mechanisms, new QoS-oriented cooperation methods and policies are studied in this work. Concretely, several models of related traffic objects we have proposed are firstly introduced briefly. Then, the scheduling policies of vehicles approaching an intersection have been presented, including three existing policies (FAFP-SV, FAFP-SQ, and HQEP-SV) and five new polices (FAFP-SQ-SV, FAFP-MQ, HWFP-SQ, HWFP-SQ-SV, HWFP-MQ). These policies combine two major factors: vehicular priority for scheduling and concurrency in traffics. The first one includes the vehicular arrival-time, priority mapped to QoS, and the weight of reserved vehicles on a lane etc. In addition, the second refers to schedule a platoon rather than single vehicle each time, or platoons on different lanes instead of one platoon on only one lane. All these policies have been implemented, and further, verified within the parameter-configurable traffic simulator QoS-CITS (v2.1) we designed and developed with C#. Abundant experiments have been conducted with configured typical traffic scenes, and experimental results show that HWFP-SQ-SV and HWFP-MQ can guarantee both the QoS of emergent vehicles and traffic throughput better than other six policies.

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“…Another interesting point is that images convey a huge amount of information. In particular, binocular vision has good environmental perception ability [8,16]. The guide path for autonomous transport machines can be extracted through recognizing the driving range, road conditions, and surroundings by binocular vision.…”

Section: Introductionmentioning

confidence: 99%

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

Wang

Liu³

2019

Journal of Sensors

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This paper proposes a 3D autonomous navigation line extraction method for field roads in hilly regions based on a low-cost binocular vision system. Accurate guide path detection of field roads is a prerequisite for the automatic driving of agricultural machines. First, considering the lack of lane lines, blurred boundaries, and complex surroundings of field roads in hilly regions, a modified image processing method was established to strengthen shadow identification and information fusion to better distinguish the road area from its surroundings. Second, based on nonobvious shape characteristics and small differences in the gray values of the field roads inside the image, the centroid points of the road area as its statistical feature was extracted and smoothed and then used as the geometric primitives of stereo matching. Finally, an epipolar constraint and a homography matrix were applied for accurate matching and 3D reconstruction to obtain the autonomous navigation line of the field roads. Experiments on the automatic driving of a carrier on field roads showed that on straight roads, multicurvature complex roads and undulating roads, the mean deviations between the actual midline of the road and the automatically traveled trajectory were 0.031 m, 0.069 m, and 0.105 m, respectively, with maximum deviations of 0.133, 0.195 m, and 0.216 m, respectively. These test results demonstrate that the proposed method is feasible for road identification and 3D navigation line acquisition.

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A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application

Cited by 51 publications

References 35 publications

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

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