Carsickness-based design and development of a controller for autonomous vehicles to improve the comfort of occupants

2020 ◽  
Vol 235 (1) ◽  
pp. 162-176
Author(s):  
Mert Sever ◽  
Namik Zengin ◽  
Ahmet Kirli ◽  
M Selçuk Arslan
Keyword(s):  
Motion Sickness ◽  
Vestibular System ◽  
Autonomous Vehicles ◽  
Quantitative Measure ◽  
Path Following ◽  
Autonomous Driving ◽  
Performance Measure ◽  
Control Approach ◽  
Lane Change Test ◽  
Iso 2631

It is anticipated that passengers in autonomous vehicles will be more occupied with in-vehicle activities. Loss of the authority on driving and engaging in non-driving tasks could cause lower predictability of car motions. This decrease in predictability is expected to increase the sensitivity to carsickness. It appears that it is crucial to develop controllers for autonomous driving with the capability of improving passenger comfort by reducing carsickness. In this regard, it can be asked how the motion variables can be used for the minimization of a carsickness-related measure, while the vehicle is required to follow a given path. In this study, an optimal control approach is being proposed to minimize a quantitative measure of carsickness. In order to address carsickness during autonomous maneuvers, the well-known motion sickness dose value formulation in ISO 2631-1 is augmented with horizontal direction motion components to define a performance measure. The performance measure includes the motion sensed in vestibular system rather than the motion occurring in the vehicle itself. Therefore, mathematical model of the vestibular system is included in the design of controller. Effects of acceleration and jerk are included in performance measure simultaneously. Control oriented linear parameter varying vehicle model is developed to design the path following controller. By means of simulation studies in which path following control is implemented, motion sickness dose values of the controlled vehicle are examined. It is shown by a regular lane change test at various speeds that the proposed controller, which seeks the minimization of the motion sickness dose value, achieves a reduction of the acceleration and jerk felt by a passenger, while the vehicle follows the given path.

Comfort-Oriented Design of Model Predictive Control in Assisted and Autonomous Driving

2020 ◽  
Author(s):  
Sara Luciani ◽  
Angelo Bonfitto ◽  
Nicola Amati ◽  
Andrea Tonoli
Keyword(s):  
Model Predictive Control ◽  
Motion Sickness ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Front Wheel ◽  
Lateral Dynamics ◽  
Iso 2631 ◽  
Yaw Angle ◽  
Simulated Scenario

Abstract This paper presents a method based on a Model Predictive Control (MPC) aiming to optimize the passenger comforts in assisted and autonomous vehicles. The controller works on the lateral and longitudinal dynamics of the car, providing front wheel steering angle and acceleration/deceleration command. The comfort is evaluated through two indexes extracted from the ISO 2631: an equivalent acceleration aeq and a Motion Sickness Dose Value (MSDV) index. The MPC weighting parameters are designed according to the values assumed by these indexes. Specifically, each weighting parameter is changed until the most satisfying comfort evaluation and the maximum vehicle performances, in terms of lateral deviation, tracking velocity and relative yaw angle, are reached. The controller is tested numerically on a simulated scenario resulting from real GPS data obtained in a highway. The method is compared with an alternative control strategy based on the combination of a PID and a Stanley control for the longitudinal and lateral dynamics, respectively. The results demonstrate the effectiveness of the approach, leading to a low percentage of passengers can experience motion sickness.

Female Voice Agents in Fully Autonomous Vehicles Are Not Only More Likeable and Comfortable, But Also More Competent

2020 ◽  
Vol 64 (1) ◽  
pp. 1033-1037
Author(s):  
Jiayuan Dong ◽  
Emily Lawson ◽  
Jack Olsen ◽  
Myounghoon Jeon
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Preference Ranking ◽  
Uncanny Valley ◽  
Tracking Data ◽  
Effective Solution ◽  
User Perception ◽  
Future Studies ◽  
Underlying Mechanisms ◽  
The Voice

Driving agents can provide an effective solution to improve drivers’ trust in and to manage interactions with autonomous vehicles. Research has focused on voice-agents, while few have explored robot-agents or the comparison between the two. The present study tested two variables - voice gender and agent embodiment, using conversational scripts. Twenty participants experienced autonomous driving using the simulator for four agent conditions and filled out subjective questionnaires for their perception of each agent. Results showed that the participants perceived the voice only female agent as more likeable, more comfortable, and more competent than other conditions. Their final preference ranking also favored this agent over the others. Interestingly, eye-tracking data showed that embodied agents did not add more visual distractions than the voice only agents. The results are discussed with the traditional gender stereotype, uncanny valley, and participants’ gender. This study can contribute to the design of in-vehicle agents in the autonomous vehicles and future studies are planned to further identify the underlying mechanisms of user perception on different agents.

Minimisation of Motion Sickness in Autonomous Vehicles

2020 ◽  
Author(s):  
Zaw Htike ◽  
Georgios Papaioannou ◽  
Efstathios Siampis ◽  
Efstathios Velenis ◽  
Stefano Longo
Keyword(s):  
Motion Sickness ◽  
Autonomous Vehicles

scholarly journals Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3783
Author(s):  
Sumbal Malik ◽  
Manzoor Ahmed Khan ◽  
Hesham El-Sayed
Keyword(s):  
Autonomous Vehicles ◽  
Communication Technologies ◽  
Autonomous Driving ◽  
Use Cases ◽  
Cooperative Driving ◽  
Vehicle To Vehicle ◽  
Future Challenges ◽  
Vehicle To Infrastructure ◽  
High Level ◽  
Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

An extended model of the ISO-2631 standard to objectify the ride comfort in autonomous driving

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S37-S45
Author(s):  
Georg Burkhard ◽  
Tobias Berger ◽  
Erik Enders ◽  
Dieter Schramm
Keyword(s):  
Ride Comfort ◽  
Autonomous Driving ◽  
Extended Model ◽  
Measuring Point ◽  
Significant Difference ◽  
Comfort Perception ◽  
Simulator Study ◽  
Iso 2631

BACKGROUND: With the development of autonomous driving, the occupants’ comfort perception and their activities during the drive are becoming increasingly the focus of research. Especially in one of the first applications, a drive on a motorway, vertical dynamics play a major role. OBJECTIVE: To be able to robustly objectify ride comfort, better models need to be developed. Initial studies have shown, that the current ISO-2631 standard creates good results in the objectification and can be regarded as benchmark. METHODS: To increase the accuracy in objectification, an extended model with the occupants’ head as additional measuring point is introduced. Instead of the known frequency filters, weighting (k-factors) is used to differentiate possible excitations. For comparing the model with the ISO-2631, a simulator study with 5 excitations and 50 inattentive subjects is carried out. RESULTS: Evaluating the study with the ISO-2631, 3 out of 5 excitations indicate a significant difference between the occupant’s impression and the calculated comfort value. In comparison the extended model has no significant difference. CONCLUSION: The results further show, that inattentive occupants move their heads significantly more. By measuring accelerations of the head, the extended model creates equivalent or more accurate comfort values than the ISO-2631.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

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