Design of a Multi-layer Lane-Level Map for Vehicle Route Planning

Liu, Chaoran; Jiang, Kun; Yang, Daoguo; Xiao, Zhongyang

doi:10.1051/matecconf/201712403001

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…An important number of map-related works for autonomous vehicles have been presented in the last decade. They range map-based localisation and navigation [12], [13], acquisition, filtering and optimization techniques for high quality maps [14] or road modelling strategies [15]. However, not many combine smooth road geometry models and map-based advanced driving assistance systems (ADAS), whose state of the art is summarized below.…”

Section: Related Workmentioning

confidence: 99%

Self-Generated OSM-Based Driving Corridors

2019

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Finding the boundaries of the drivable space is a key to the development of any advanced driver assistance systems with automated driving functions. A common approach found in the literature is to combine the information of digital maps with multiple on-board sensors for building a robust and accurate model of the environment from which to extract the navigable space. In this sense, the digital map is the crucial component for relating the location of the vehicle and identifying the different road features. This paper presents an automatic procedure for generating driving corridors from OpenStreetMap. The proposed method expands the original map representation, replacing polylines by polynomial-based roads, whose sections are defined using cubic Bézier curves. All curves are automatically adjusted from the original road description, thus generating an efficient and accurate road representation without human intervention. Finally, the driving corridors are generated as a concatenation of the modified road sections along a planned route. The proposed approach has been validated in a peri-urban environment, for which corridors where successfully generated in all cases.

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Section: Related Workmentioning

confidence: 99%

Self-Generated OSM-Based Driving Corridors

2019

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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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“…In China, 19 companies have conducted investigations in lane-level mapping since 2019. Compared with road-level maps, lane-level maps contain rich lane data [7], with accuracy ranging from a few meters to the decimeter or even centimeter level under various autonomous vehicle functions [8]. The road network is an important aspect of maps and plays an important role in intelligent driving projects [9].…”

Section: Introductionmentioning

confidence: 99%

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Zheng

Song²,

et al. 2019

IJGI

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With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road.

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Design of a Multi-layer Lane-Level Map for Vehicle Route Planning

Cited by 7 publications

References 10 publications

Self-Generated OSM-Based Driving Corridors

Self-Generated OSM-Based Driving Corridors

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

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

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