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Sensors and Sensing for Automated Driving
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Sensors and Sensing for Automated Driving

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 12117

Special Issue Editors

Dr. Zheng Wang

E-Mail Website
Guest Editor
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: driver assistance systems; human system interaction; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals
Dr. Edric John Cruz Nacpil

E-Mail Website
Guest Editor
Corpy & Co., Inc., Tokyo 113-0033, Japan
Interests: application of sensors and signal processing to autonomous vehicles; artificial intelligence for autonomous driving; human–machine interfaces for automobiles; biomechanics and human factors of driving
Dr. Hongyu Hu

E-Mail Website
Guest Editor
State Key Laboratory of Automotive Simulation and Control, Jilin University, Renmin Street No.5988, Changchun 130022, China
Interests: connected & automated vehicles; driver behavior analysis; human-machine interaction
Prof. Dr. Rencheng Zheng

E-Mail Website
Guest Editor
Key Laboratory of Mechanism Theory and Equipment Design of Ministry, Tianjin University, Tianjin 300072, China
Interests: automated platooning; human-machine interface; driver behaviors; sensors; signal processing

Special Issue Information

Dear Colleagues,

Automated driving is becoming increasingly available through the advancement of information and sensing technology. Automated vehicles are informed by imaging, global satellite navigation, computing networks and other systems relying on sensors to gather and process data about vehicle dynamics and surroundings. Given the demand for data from sensor technology, this Special Issue aims to contribute the latest research to Sensors regarding sensor and sensing technology development for current and future automated driving. For example, the onboard sensing systems of some automated vehicles receive and process raw sensor data into useful information with artificial neural networks so that vehicles can analyze driving environments and operate safely. Current commercial and experimental systems employing biosignal sensors have been developed to monitor drowsiness, distracted driving, and other driving safety parameters. There is also a growing body of research on sensors to enable safer, more trustworthy, and personalized human–computer interaction in future automated vehicles. In addition to sensor systems pertaining to vehicle operation, consideration is thus open to wearable sensors, devices and electronics for vehicle occupants. In order to ultimately provide useful information for continued technological advancement, this Special Issue welcomes studies and reviews from a wide breadth of research spanning experimental and commercial settings.

Dr. Zheng Wang
Dr. Edric John Cruz Nacpil 
Dr. Hongyu Hu 
Prof. Dr. Rencheng Zheng
Guest Editors

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Keywords

  • automated driving
  • advacned driver assistance systems
  • driving safety
  • machine learning applied in automated driving
  • information fusion
  • object detection and tracking
  • V 2 X sensing
  • sensing and imaging
  • signal processing
  • intelligent sensors
  • human–machine interfaces

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Published Papers (3 papers)

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Research

22 pages, 26866 KiB  
Article
A Kamm’s Circle-Based Potential Risk Estimation Scheme in the Local Dynamic Map Computation Enhanced by Binary Decision Diagrams
by Arvind Kumar and Hiroaki Wagatsuma
Sensors 2022, 22(19), 7253; https://doi.org/10.3390/s22197253 - 24 Sep 2022
Cited by 1 | Viewed by 2416
Abstract
Autonomous vehicles (AV) are a hot topic for safe mobility, which inevitably requires sensors to achieve autonomy, but relying too heavily on sensors will be a risk factor. A high-definition map (HD map) reduces the risk by giving geographical information if it covers [...] Read more.
Autonomous vehicles (AV) are a hot topic for safe mobility, which inevitably requires sensors to achieve autonomy, but relying too heavily on sensors will be a risk factor. A high-definition map (HD map) reduces the risk by giving geographical information if it covers dynamic information from moving entities on the road. Cooperative intelligent transport systems (C-ITS) are a prominent approach to solving the issue and local dynamic maps (LDMs) are expected to realize the ideal C-ITS. An actual LDM implementation requires a fine database design to be able to update the information to represent potential risks based on future interactions of vehicles. In the present study, we proposed an advanced method for embedding the geographical future occupancy of vehicles into the database by using a binary decision diagram (BDD). In our method, the geographical future occupancy of vehicles was formulated with Kamm’s circle. In computer experiments, sharing BDD-based occupancy data was successfully demonstrated in the ROS-based simulator with the linked list-based BDD. Algebraic operations in exchanged BDDs effectively managed future interactions such as data insertion and timing of collision avoidance in the LDM. This result opened a new door for the realization of the ideal LDM for safety in AVs. Full article
(This article belongs to the Special Issue Sensors and Sensing for Automated Driving)
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14 pages, 5696 KiB  
Article
External Human–Machine Interfaces for Autonomous Vehicles from Pedestrians’ Perspective: A Survey Study
by Jiawen Guo, Quan Yuan, Jingrui Yu, Xizheng Chen, Wenlin Yu, Qian Cheng, Wuhong Wang, Wenhui Luo and Xiaobei Jiang
Sensors 2022, 22(9), 3339; https://doi.org/10.3390/s22093339 - 27 Apr 2022
Cited by 9 | Viewed by 4214
Abstract
With the increasing number of automated vehicles (AVs) being tested and operating on roads, external Human–Machine Interfaces (eHMIs) are proposed to facilitate interactions between AVs and other road users. Considering the need to protect vulnerable road users, this paper addresses the issue by [...] Read more.
With the increasing number of automated vehicles (AVs) being tested and operating on roads, external Human–Machine Interfaces (eHMIs) are proposed to facilitate interactions between AVs and other road users. Considering the need to protect vulnerable road users, this paper addresses the issue by providing research evidence on various designs of eHMIs. Ninety participants took part in this experiment. Six sets of eHMI prototypes—Text, Arrowed (Dynamic), Text and Symbol, Symbol only, Tick and Cross and Traffic Lights, including two sub-designs (Cross and Do Not Cross)—were designed. The results showed that 65.1% of participants agreed that external communication would have a positive effect on pedestrians’ crossing decisions. Among all the prototypes, Text, and Text and Symbol, eHMIs were the most widely accepted. In particular, for elderly people and those unfamiliar with traffic rules, Text, and Text and Symbol, eHMIs would lead to faster comprehension. The results confirmed that 68.5% of participants would feel safer crossing if the eHMI had the following features: ‘Green’, ‘Text’, ‘Symbol’, or ‘Dynamic’. These features are suggested in the design of future systems. This research concluded that eHMIs have a positive effect on V2X communication and that textual eHMIs were clear to pedestrians. Full article
(This article belongs to the Special Issue Sensors and Sensing for Automated Driving)
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18 pages, 103486 KiB  
Article
Prediction of Pedestrian Crossing Behavior Based on Surveillance Video
by Xiao Zhou, Hongyu Ren, Tingting Zhang, Xingang Mou, Yi He and Ching-Yao Chan
Sensors 2022, 22(4), 1467; https://doi.org/10.3390/s22041467 - 14 Feb 2022
Cited by 10 | Viewed by 4340
Abstract
Prediction of pedestrian crossing behavior is an important issue faced by the realization of autonomous driving. The current research on pedestrian crossing behavior prediction is mainly based on vehicle camera. However, the sight line of vehicle camera may be blocked by other vehicles [...] Read more.
Prediction of pedestrian crossing behavior is an important issue faced by the realization of autonomous driving. The current research on pedestrian crossing behavior prediction is mainly based on vehicle camera. However, the sight line of vehicle camera may be blocked by other vehicles or the road environment, making it difficult to obtain key information in the scene. Pedestrian crossing behavior prediction based on surveillance video can be used in key road sections or accident-prone areas to provide supplementary information for vehicle decision-making, thereby reducing the risk of accidents. To this end, we propose a pedestrian crossing behavior prediction network for surveillance video. The network integrates pedestrian posture, local context and global context features through a new cross-stacked gated recurrence unit (GRU) structure to achieve accurate prediction of pedestrian crossing behavior. Applied onto the surveillance video dataset from the University of California, Berkeley to predict the pedestrian crossing behavior, our model achieves the best results regarding accuracy, F1 parameter, etc. In addition, we conducted experiments to study the effects of time to prediction and pedestrian speed on the prediction accuracy. This paper proves the feasibility of pedestrian crossing behavior prediction based on surveillance video. It provides a reference for the application of edge computing in the safety guarantee of automatic driving. Full article
(This article belongs to the Special Issue Sensors and Sensing for Automated Driving)
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