Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

Gwon, Gi-Poong; Hur, Woo-Sol; Kim, Seong-Woo; Seo, Seung‐Woo

doi:10.1109/tvt.2016.2535210

Cited by 81 publications

(61 citation statements)

References 46 publications

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“…In the scenario of vehicle/robot navigation in outdoor environments, a different positioning method should be employed. Sensors, such as GPS, inertial measurement units, LIDAR, and cameras, have been employed for this purpose [1,2,7], as described in the introduction section. …”

Section: Methodsmentioning

confidence: 99%

“…When lane markings are available, lane-detection systems, based on LIDAR [3], and/or cameras [4,5], can provide lateral distance measurements from the lane markings to the vehicle for lane-level navigation [6]. When load maps that contain accurate geometry of all lanes and the three-dimensional (3D) structure of roads, such as overpasses, are known in advance, navigation accuracy can be improved [7]. In unknown environments without lane markings, such as basements and suburbs, another navigation approach needs to be developed.…”

Section: Introductionmentioning

confidence: 99%

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Navigation of an Autonomous Wheeled Robot in Unknown Environments Based on Evolutionary Fuzzy Control

Chou

Juang

2018

Inventions

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Navigation of a wheeled robot in unknown environments is proposed in this paper. The approach may be applied to navigating an autonomous vehicle in unknown environments, such as parking lots. The navigation consists of three parts: obstacle avoidance behavior, target seeking behavior, and a behavior supervisor. The obstacle avoidance behavior is achieved by controlling the robot to move along an obstacle boundary through evolutionary fuzzy control. In the evolutionary fuzzy control approach, a Pareto set of fuzzy controllers (FCs) is found though a multi-objective continuous ant colony optimization algorithm. Target seeking behavior is achieved by controlling the robot through hybrid proportional-integral-derivative (PID) controllers. The behavior supervisor determines the switching between obstacle avoidance and target seeking behaviors, where the dead-cycle problem is considered. Simulations and experiments were performed to verify the effectiveness of the proposed navigation scheme.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Navigation of an Autonomous Wheeled Robot in Unknown Environments Based on Evolutionary Fuzzy Control

Chou

Juang

2018

Inventions

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“…For example, a method based on laser point cloud data and GPS data can combine sensor and position data to extract a road network. Gwon et al extracted road information from a three-dimensional (3D) laser radar and presented a road-map-generation system that simultaneously considers accuracy, storage efficiency, and usability [26]. Another example is combining image and GPS data to extract road networks.…”

Section: Lane-level Road Network Generation With a Probe Vehiclementioning

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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“…In urban roads, an autonomous car must stay within a lane in order to make space for other vehicles and, to achieve that, it must have some sort of internal map of the roads' lanes. Humans make use of the horizontal signalization of roads (road surface marking) to try and stay within lane while driving and there are many works in the literature about how to automatically detect relevant horizontal signalization for building ADAS systems [1]- [4] or relevant parts of autonomous cars systems [5]- [8]. However, sometimes the horizontal signalization is not in good conditions or even absent.…”

Section: Introductionmentioning

confidence: 99%

Mapping Road Lanes Using Laser Remission and Deep Neural Networks

Carneiro

Nascimento

Guidolini

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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We propose the use of deep neural networks (DNN) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization, in order to allow the operation of autonomous cars in such situations. We take a segmentation approach to the problem and use the Efficient Neural Network (ENet) DNN for segmenting LiDAR remission grid maps into road maps. We represent road maps using what we called road grid maps. Road grid maps are square matrixes and each element of these matrixes represents a small square region of real-world space. The value of each element is a code associated with the semantics of the road map. Our road grid maps contain all information about the roads' lanes required for building the Road Definition Data Files (RDDFs) that are necessary for the operation of our autonomous car, IARA (Intelligent Autonomous Robotic Automobile). We have built a dataset of tens of kilometers of manually marked road lanes and used part of it to train ENet to segment road grid maps from remission grid maps. After being trained, ENet achieved an average segmentation accuracy of 83.7%. We have tested the use of inferred road grid maps in the real world using IARA on a stretch of 3.7 km of urban roads and it has shown performance equivalent to that of the previous IARA's subsystem that uses a manually generated RDDF.

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Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

Cited by 81 publications

References 46 publications

Navigation of an Autonomous Wheeled Robot in Unknown Environments Based on Evolutionary Fuzzy Control

Navigation of an Autonomous Wheeled Robot in Unknown Environments Based on Evolutionary Fuzzy Control

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

Mapping Road Lanes Using Laser Remission and Deep Neural Networks

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