Predictive Cruise Control Using High-Definition Map and Real Vehicle Implementation

Chu, Hongqing; Guo, Lulu; Gao, Bingzhao; Bian, Ning; Zhou, Jianguang

doi:10.1109/tvt.2018.2871202

Cited by 77 publications

(24 citation statements)

References 34 publications

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“…As an effective augmentation tool among on-board sensors, the HD compact map has gained tremendous popularity as a consumer vehicle add-on feature. With pre-collected environmental information, the HD map can act as a virtual sensor to improve vehicle safety without incurring additional hardware system complexity [4]. As a result, high-precision HD maps are considered the cornerstone of IV technology, especially for more advanced automated vehicles [2].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…In [23], maps were classified into two categories: planar and point-cloud maps. Planar maps describe the geographic world with layers or planes on Geographic Information System (GIS) software, such as high-definition (HD) maps [24] or lane-level maps [25]. Lane-level maps are enhanced with lane-level details of the environment for autonomous driving compared with road-level maps.…”

Section: Introductionmentioning

confidence: 99%

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Zheng

Yang

et al. 2019

Sustainability

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Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review.

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“…This strategy is based on high-definition (HD) map and comprises MPCbased and stop-and-go controllers. The MPCbased controller is discussed in [7]. The present study aims to discuss stop-and-go controller and provide important technical details that are not covered in [7].…”

mentioning

confidence: 99%

“…The MPCbased controller is discussed in [7]. The present study aims to discuss stop-and-go controller and provide important technical details that are not covered in [7]. The main contributions of this study are summarized as follows.…”

mentioning

confidence: 99%

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Energy-efficient longitudinal driving strategy for intelligent vehicles on urban roads

Chu

Guo

Yan

et al. 2019

Sci. China Inf. Sci.

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Predictive Cruise Control Using High-Definition Map and Real Vehicle Implementation

Cited by 77 publications

References 34 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

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

Energy-efficient longitudinal driving strategy for intelligent vehicles on urban roads

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