scholarly journals User Monitoring in Autonomous Driving System Using Gamified Task: A Case for vr/AR in-Car Gaming

2021 ◽  
Author(s):  
Joseph K. Muguro ◽  
Pringgo Widyo Laksono ◽  
Yuta Sasatake ◽  
Kojiro Matsushita ◽  
Minoru Sasaki
Keyword(s):  
Real World ◽  
Secondary Task ◽  
Autonomous Driving ◽  
Driving Simulation ◽  
Control Loop ◽  
Automated Driving ◽  
Driving System ◽  
The Road ◽  
Current Test ◽  
Autonomous Driving System

As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. The paper’s objective is to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. From the results, there was less than 1 second difference between the means of gaming task (mean = 2.55s, std = 0.1002s) to no-task (mean = 2.55s, std = 0.1002s). Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments AR-Game focusing on real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

scholarly journals User Monitoring in Autonomous Driving System Using Gamified Task: A Case for VR/AR In-Car Gaming

2021 ◽  
Vol 5 (8) ◽  
pp. 40
Author(s):  
Joseph K. Muguro ◽  
Pringgo Widyo Laksono ◽  
Yuta Sasatake ◽  
Kojiro Matsushita ◽  
Minoru Sasaki
Keyword(s):  
Secondary Task ◽  
Autonomous Driving ◽  
Driving Simulation ◽  
Control Loop ◽  
Automated Driving ◽  
Driving System ◽  
The Road ◽  
Current Test ◽  
Significant Difference ◽  
Autonomous Driving System

Background: As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. This paper aims to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. Methods: We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. Results: There was a significant difference in the tasks (F(2,33) = 4.34, p = 0.0213), identifying the game-task as significant with respect to reaction time and ideal for the present investigation. Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. Conclusion: The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments with AR-Games focusing on the real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

Autonomy and Responsibility in Hybrid Systems

2017 ◽  
Author(s):  
Wulf Loh ◽  
Janina Loh
Keyword(s):  
Hybrid Systems ◽  
Hybrid System ◽  
Moral Dilemma ◽  
Autonomous Driving ◽  
Shared Responsibility ◽  
Driving System ◽  
Autonomous Cars ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Autonomous Driving System

In this chapter, we give a brief overview of the traditional notion of responsibility and introduce a concept of distributed responsibility within a responsibility network of engineers, driver, and autonomous driving system. In order to evaluate this concept, we explore the notion of man–machine hybrid systems with regard to self-driving cars and conclude that the unit comprising the car and the operator/driver consists of such a hybrid system that can assume a shared responsibility different from the responsibility of other actors in the responsibility network. Discussing certain moral dilemma situations that are structured much like trolley cases, we deduce that as long as there is something like a driver in autonomous cars as part of the hybrid system, she will have to bear the responsibility for making the morally relevant decisions that are not covered by traffic rules.

Oops! It's Too Late. Your Autonomous Driving System Needs a Faster Middleware

2021 ◽  
Vol 6 (4) ◽  
pp. 7301-7308
Author(s):  
Tianze Wu ◽  
Baofu Wu ◽  
Sa Wang ◽  
Liangkai Liu ◽  
Shaoshan Liu ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Driving System ◽  
Autonomous Driving System

An Autonomous Driving System for Unknown Environments Using a Unified Map

2015 ◽  
Vol 16 (4) ◽  
pp. 1999-2013 ◽  
Author(s):  
Inwook Shim ◽  
Jongwon Choi ◽  
Seunghak Shin ◽  
Tae-Hyun Oh ◽  
Unghui Lee ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Driving System ◽  
Unknown Environments ◽  
Autonomous Driving System

UGV autonomous driving system design for unstructed environment

2021 ◽  
Author(s):  
Jingqin Zhang ◽  
Jun Hou ◽  
Jinwen Hu ◽  
Chunhui Zhao ◽  
Zhao Xu ◽  
...  
Keyword(s):  
System Design ◽  
Autonomous Driving ◽  
Driving System ◽  
Autonomous Driving System

An autonomous driving system utilizing image processing accelerated by FPGA

2021 ◽  
Author(s):  
Kazunari Takasaki ◽  
Kota Hisafuru ◽  
Ryotaro Negishi ◽  
Kazuki Yamashita ◽  
Keisuke Fukada ◽  
...  
Keyword(s):  
Image Processing ◽  
Autonomous Driving ◽  
Driving System ◽  
Autonomous Driving System

A Modular Autonomous Driving System for Electric Boats based on Fuzzy Controllers and Q-Learning

2021 ◽  
Author(s):  
Emanuele Ferrandino ◽  
Antonino Capillo ◽  
Enrico De Santis ◽  
Fabio Mascioli ◽  
Antonello Rizzi
Keyword(s):  
Autonomous Driving ◽  
Fuzzy Controllers ◽  
Driving System ◽  
Q Learning ◽  
Autonomous Driving System

Graceful Degradation Design Process for Autonomous Driving System

2019 ◽  
pp. 19-34 ◽  
Author(s):  
Tasuku Ishigooka ◽  
Satoshi Otsuka ◽  
Kazuyoshi Serizawa ◽  
Ryo Tsuchiya ◽  
Fumio Narisawa
Keyword(s):  
Design Process ◽  
Autonomous Driving ◽  
Graceful Degradation ◽  
Driving System ◽  
Autonomous Driving System
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