Roadgraph Generation and Free-Space Estimation in Unknown Structured Environments for Autonomous Vehicle Motion Planning

Keßler, Tobias; Minnerup, Pascal; Esterle, Klemens; Feist, Christian; Mickler, Florian; Roth, Erwin; Knoll, Alois

doi:10.1109/itsc.2018.8569306

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…da Silva and de Sousa [92] used dynamic programming for AV motion control, where the objective is to follow a desired path while keeping the shortest distance between the vehicle and the desired path under some predefined threshold. Kessler et al [93] introduced two novel approaches for extracting a topological road-graph with possible intersection options from sensor data, along with a geometric representation of the available maneuvering space. Also, a search and optimization-based path planning method for guiding the vehicle along a selected track in the road-graph and within the free-space is presented.…”

Section: Motion Planningmentioning

confidence: 99%

Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis

Hacohen

Medina

Shoval

2022

IEEE Trans. Intell. Transport. Syst.

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Autonomous Driving (AD) introduces dramatic changes to the way we travel. This emerging technology has the potential to impact the transportation sector across a wide array of categories including safety, efficiency, congestion, legislation, and travel behavior. In this survey, we review the main issues involved in AD as discussed in the literature, and shed light on topics that we consider requiring further development based on Google Scholar and the Web of Science (WoS) data base. The paper also provides the results of research trends related to Autonomous Driving based on analysis of the number of search results listed in google trends. According to our research, the fields of Vehicle-to-Vehicle (V2V) and Vehicle-to-Cloud (V2C) networking are of higher interest due to the technological gaps and standardization processes. In addition, cyber and security research is in acceleration due to its importance.

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Section: Motion Planningmentioning

confidence: 99%

Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis

Hacohen

Medina

Shoval

2022

IEEE Trans. Intell. Transport. Syst.

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“…From this, especially the available free-space for maneuvering and a set of possible reference tracks to follow has to be available. These can be drawn from a high-definition map or a system proposed by Kessler et al [14] can be used.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Multi-Vehicle Behavior Coordination for Autonomous Driving

Keßler

Knoll

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Creating rational driving options and designing the decision process to select the best solution in a traffic situation with multiple participants present is a challenging problem. Other participants could be cooperating communicationenabled autonomous vehicles or vehicles controlled by human drivers with egoistic goals.This work introduces a novel approach to coordinate the behavior of multiple vehicles in generic traffic scenes. Our three-step method generates motion options neglecting vehicle interactions at first. Afterward, a mixed-integer linear optimization problem is solved to find the optimally coordinated motion patterns, followed by an online re-calibration based on the observed behaviors in reality.We demonstrate and evaluate the applicability in an evasive maneuver requiring vehicle interaction in detail and also present an intersection scenario. We further show that cooperative behavior, as well as egoistic driver intentions, can be handled safely and analyze the properties of the proposed solution.

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“…Recently, various studies on autonomous vehicles have been conducted in various fields. There are papers on various signal processing methods of LiDAR sensors mainly used in autonomous vehicles [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Among them, there are many papers that presented research on free space sensing.…”

Section: Introductionmentioning

confidence: 99%

Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

Lee

You

2021

Sensors

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In this paper, we propose a new free space detection algorithm for autonomous vehicle driving. Previous free space detection algorithms often use only the location information of every frame, without information on the speed of the obstacle. In this case, there is a possibility of creating an inefficient path because the behavior of the obstacle cannot be predicted. In order to compensate for the shortcomings of the previous algorithm, the proposed algorithm uses the speed information of the obstacle. Through object tracking, the dynamic behavior of obstacles around the vehicle is identified and predicted, and free space is detected based on this. In the free space, it is possible to classify an area in which driving is possible and an area in which it is not possible, and a route is created according to the classification result. By comparing and evaluating the path generated by the previous algorithm and the path generated by the proposed algorithm, it is confirmed that the proposed algorithm is more efficient in generating the vehicle driving path.

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Roadgraph Generation and Free-Space Estimation in Unknown Structured Environments for Autonomous Vehicle Motion Planning

Cited by 3 publications

References 14 publications

Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis

Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis

Cooperative Multi-Vehicle Behavior Coordination for Autonomous Driving

Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

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