Visibility analysis for autonomous vehicle comfortable navigation

Morales, Yoichi; Even, Jani; Kallakuri, Nagasrikanth; Ikeda, Teruyuki; Shinozawa, Kazuhiko; Kondo, Tadahisa; Hagita, Norihiro

doi:10.1109/icra.2014.6907162

Cited by 30 publications

(5 citation statements)

References 12 publications

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“…Morales et al (2013) presented a "comfort map" that integrates the human factor into the SLAM framework. Subsequently, Morales et al (2014Morales et al ( , 2015 investigated the comfort level through visibility analysis to plan trajectories that minimize the discomfort level.…”

Section: Mobile Robot For Assistive Technologymentioning

confidence: 99%

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Liu

Huang

et al. 2021

Front. Robot. AI

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Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)–based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed.

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Section: Mobile Robot For Assistive Technologymentioning

confidence: 99%

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Liu

Huang

et al. 2021

Front. Robot. AI

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“…In addition to supporting localization and navigation, HP 3D maps can be leveraged for lane network construction, dynamic object detection, prediction of motion of vulnerable road users, and visibility analysis for the correlation between environmental visibility and navigational comfort under autonomous driving [28,29]. The so-called self-healing mapping systems allows a detailed inventory of road features and objects, part of road infrastructure, on the side of the road for real-time determination of infrastructure visibility.…”

Section: High-precision 3d Mapsmentioning

confidence: 99%

Visibility-Based Technologies and Methodologies for Autonomous Driving

Easa¹,

Ma²,

Elshorbagy³

et al. 2021

Artificial Intelligence

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The three main elements of autonomous vehicles (AV) are orientation, visibility, and decision. This chapter presents an overview of the implementation of visibility-based technologies and methodologies. The chapter first presents two fundamental aspects that are necessary for understanding the main contents. The first aspect is highway geometric design as it relates to sight distance and highway alignment. The second aspect is mathematical basics, including coordinate transformation and visual space segmentation. Details on the Light Detection and Ranging (Lidar) system, which represents the ‘eye’ of the AV are presented. In particular, a new Lidar 3D mapping system, that can be operated on different platforms and modes for a new mapping scheme is described. The visibility methodologies include two types. Infrastructure visibility mainly addresses high-precision maps and sight obstacle detection. Traffic visibility (vehicles, pedestrians, and cyclists) addresses identification of critical positions and visibility estimation. Then, an overview of the decision element (path planning and intelligent car-following) for the movement of AV is presented. The chapter provides important information for researchers and therefore should help to advance road safety for autonomous vehicles.

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“…There are works which describe computational solutions for autonomous vehicles when navigating areas with high a likelihood of collisions with other vehicles, pedestrians and cyclists [15]. Morales et al computed visibility through an index [16] and created a trajectory planner which considers occluded and non-occluded features of the environment [17].…”

Section: A Proactive Drivingmentioning

confidence: 99%

Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning

Sama

Morales

Liu

et al. 2020

IEEE Trans. Veh. Technol.

Self Cite

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This paper introduces a method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors. Deep learning techniques were used to extract latent features from the collected data. Extracted features were clustered into behaviors and used to create velocity profiles allowing an autonomous driving agent could drive in a human-like manner. By using proactive driving behaviors, the agent could limit potential sources of discomfort such as jerk and uncomfortable velocities. Additionally, we proposed a method to compare trajectories where not only the geometric similarity is considered, but also velocity, acceleration and jerk. Experimental results in a simulator implemented in ROS show that the autonomous agent built with the driving behaviors was capable of driving similarly to expert human drivers.Index Terms-Autonomous driving, autoencoder, driving behavior, deep learning. I. INTRODUCTIONI N RECENT years, there has been increased interest in Autonomous Driving. Large corporations, such as Waymo, Tesla, Cruise, and Uber, are investing heavily into it [2]. Traditional model-based techniques for autonomous driving vehicles use maps to localize themselves, plan and follow paths to their Manuscript

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Visibility analysis for autonomous vehicle comfortable navigation

Cited by 30 publications

References 12 publications

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People

Visibility-Based Technologies and Methodologies for Autonomous Driving

Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning

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