2019年虚拟现实与汽车自动驾驶外文翻译中英文.pdf

使用虚拟现实进行汽车自动驾驶中英文 2019 英文 Get ready for automated driving using Virtual Reality Daniele Sportillo,Alexis Paljic,Luciano Ojeda Abstract In conditionally automated vehicles,drivers can engage in secondary activities while traveling to their destination.However,drivers are required to appropriately respond,in a limited amount of time,to a take-over request when the system reaches its functional boundaries.Interacting with the car in the proper way from the first ride is crucial for car and road safety in general.For this reason,it is necessary to train drivers in a risk-free environment by providing them the best practice to use these complex systems.In this context,Virtual Reality(VR)systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved.In addition,Head-Mounted Display(HMD)-based VR(light VR)would allow for the easy deployment of such training systems in driving schools or car dealerships.In this study,the effectiveness of a light Virtual Reality training program for acquiring interaction skills in automated cars was investigated.The effectiveness of this training was compared to a user manual and a fixed-base simulator with respect to both objective and self-reported measures.Sixty subjects were randomly assigned to one of the systems in which they went through a training phase followed by a test drive in a high-end driving simulator.Results show that the training system affects the take-over performances.Moreover,self-reported measures indicate that the light VR training is preferred with respect to the other systems.Finally,another important outcome of this research is the evidence that VR plays a strategic role in the definition of the set of metrics for profiling proper driver interaction with the automated vehicle.Keywords:Conditionally automated vehicles,Virtual Reality,Head-Mounted Display,Take-over request,Training 1.Introduction Imagine you are reading this article in your car as you drive on the highway.Suddenly,your car asks you to“take-over”.What would you do?At the time of writing,this scenario breaks numerous laws and is potentially very dangerous.In the future,it would not only be legal and safe,but you would likely know how to react to your cars demands to hand over control,keeping yourself,passengers,and other vehicles out of harms way.In future automated vehicles the above situation would be fairly common.In particular,conditionally automated vehicles(SAE Level-3 S.International(2017)do not require drivers to constantly monitor their driving environment;they can,therefore,engage in secondary activities such as reading,writing emails and watching videos.However,when the automated system encounters unexpected situations,it will assume that drivers who are sufficiently warned will adequately respond to a take-over request.The reestablishment of the driving context(i.e.rapid onboarding)is one challenge of conditionally automated vehicles(Casner et al.,2016)for the car industry.The revolution of the driving activity,the complexity of these new systems and the variety of situations that the driver can face requires that drivers must have already acquired the core skills necessary to securely interact with the automated car before their first ride.Establishing drivers role and avoiding confusion(Noy et al.,2018)is crucial for the safety of both the drivers themselves and other road users.At present,a vehicles functionalities are demonstrated to customers via an informal presentation by the car dealer during the hand-over process;for further information,customers are required to read the car owners manual.For an automated vehicle,these traditional procedures would not be feasible to familiarize the new car owner with the automated system,primarily because the acquisition of skills by the customer is not ensured.In addition,car dealers themselves must be trained and kept up to date of each new version of the system.In this context,Virtual Reality(VR)constitutes a potentially valuable learning and skill assessment tool which would allow drivers to familiarize themselves with the automated vehicle and interact with the novel equipment involved in a free-risk environment.VR allows for the possibility of encountering dangerous driving conditions without putting the driver at physical risk and enable the controllabilityand reproducibility of the scenario conditions(De Winter et al.,2012).VR has usually been associated with high costs and huge computational power.For these reasons immersive training based on CAVEs or Head-Mounted Displays has until now been prohibitive in mainstream settings.However,in recent years,technological progress and the involvement of dominant technology companies has allowed the development of affordable VR devices.The objective of this research is to explore the potential of the role of light Virtual Reality systems,in particular,for the acquisition of skills for the Transfer of Control(ToC)in highly automated cars.By using the adjective light,we want to mark the difference between VR systems that are portable and/or easy to set up(HMDs,mobile VR)and systems that are cumbersome and require dedicated space to operate(CAVE systems).The idea is that thanks to the portability and the cost-effectiveness,light VR systems could be easily deployed in car dealerships to train a large amount of people in an immersive environment in a safe and reliable way.The light VR system proposed in this paper consists of a consumer HMD and a racing wheel.This paper aims to compare the effectiveness of a training program based on this system with a user manual and with a fixed-base driving simulator.To validate the light VR system,user performances are evaluated during a test drive in a high-end driving simulator and self-reported measures are collected via questionnaires.1.1.Related work Virtual Reality has been extensively used to train professionals and non-professionals in various domains.The unique characteristics of learning in the 3D environment provided by immersive VR systems such as CAVEs or HMDs,can enable learning tasks that are not possible or not as effective in 2D environments provided by traditional desktop monitors.Dalgarno and Lee(2010)highlighted the benefits of this kind of 3D Virtual Learning Environments(3D VLEs)by proposing a model based on their distinctive features such as the representational fidelity and the learner interaction.More in detail,HMD-based VR turns out to be more effective when compared to other training systems,for a wide range of applications such as surgery(Hamilton et al.,2002)(HMD compared to video trainer),aircraft visual inspection(Vora et al.,2002)(HMD compared to PC-based training tool),power production(Avveduto et al.,2017)(HMD compared to traditional training),mining industry(Zhang,2017)(HMD compared to screen-based and projector-base training).When it comes to driving simulation(DS),VR is used to study several aspects of the driving task.In this context,moving-base simulators(Lee et al.,1998)are preferable to fixed-base simulators(Milleville-Pennel and Charron,2015,Fisher et al.,2002)for their closer approach to real-world driving(Klver et al.,2016).By investigating the physical,behavioral and cognitive validity of these kind of simulators with respect to the real driving task(Milleville-Pennel and Charron,2015),it has been also shown that DS can be a useful tool for the initial resumptionof driving,because it helps to avoid stress that may lead to task failure or deterioration in performance.Although most of the studies in DS uses static screens as the display system,recent studies prove that HMD-based DS leads to similar physiological response and driving performance when compared to stereoscopic 3D or 2D screens(Weidner et al.,2017).Taheri et al.(2017)presented a VR DS system composed of HMD,steering wheel and pedals to analyze drivers characteristics;Goedicke et al.(2018)instead proposed an implementation of an HMD in a real car to simulate automated driving as the vehicle travels on a road.Even if the steering wheel is the most used driving interface,novel HMD systems usually come with wireless 6-DoF controllers which can be used to control a virtual car.In a pilot study,Sportillo et al.(2017)compare steering wheel and controller-based interaction in HMD-based driving simulators.The authors conclude that even though objective measures do not provide decisive parameters for determining the most adequate interaction modality,self-report indicators show a significant difference in favor of the steering wheel.Among other things,DS provides the opportunity to implement,in a forgiving environment,critical scenarios and hazardous situations which are ethically not possible to evaluate on real roads(Ihemedu-Steinke et al.,2017b).For this reason and to overcome the limited availability of physical prototypes for research purposes,DS is extensively used for studies on automated vehicles to design future automotive HMI(Melcher et al.,2015)for Take-Over Requests(TORs)and to investigate the behavioral responses during the transition from automated to manual control(Merat et al.,2014).A research area that is gaining interest in the automated driving community concerns the impact of non-driving activities on take-over performance.To study drivers distraction during automated driving,researchers generally use standardized and naturalistic tasks.Standardized tasks(such as the cognitive n-back task(Happee et al.,2017),the SuRT task(Happee et al.,2017,Gold et al.,2013),the Twenty Questions Task(TQT)(Krber et al.,2016)provide experimental control,but they do not usually correspond to what the driver will do in the vehicle.Naturalistic tasks,instead,provide ecological validity,but they could introduce experimental bias.Important findings were found by Zeeb et al.(2016)who studied how visual-cognitive load impacts take-over performance by examining the engagement in three different naturalistic secondary tasks(writing an email,reading a news text,and watching a video clip).The authors found that the drivers engagement in secondary tasks only slightly affected the time required to regain the control of the vehicle,but non-distracted drivers performed better in the lane-keeping task.Most of the studies in this domain implement safety-critical take-over scenarios caused by an obstacle(usually a broken down vehicle)on the current lane(Zeeb et al.,2016,Sportillo et al.,2017,Happee et al.,2017,Navarro et al.,2016,Krber et al.,2016)and non-critical scenarios caused by the absence of lane markings(Zeeb et al.,2016,Payre et al.,2017).To ensure security and to succeed in the take-over process,it is important to understand how much time before a system boundary a driver who is out of the loop should be warned.Gold et al.(2013)indicate that shorter TOR-time leads to a faster but worse reaction.However,assessing the quality of the take-over performance remains an open problem.Reaction times(such as gaze reaction time,hands on wheel time,and intervention time)are analyzed(Happee et al.,2017).Time To Collision,lateral accelerations and minimum clearance towards are objective metrics used in obstacle avoidance scenarios(Happee et al.,2017).Concerning subjective measures,drivers are usually asked to reply to questionnaires:the Driver Skill Inventory(DSI)(Spolander,1983)and Driver Behaviour Questionnaire(DBQ)(Reason et al.,1990)have been largely used to evaluate the self-assessment of driving skills(Roy and Liersch,2013)in the last decades.In recent studies,questionnaires have been used to investigate the importance of initial skilling and to predict the deskilling in automated vehicles(Trsterer et al.,2016).In the same field,surveys have also been used to evaluate usefulness and satisfaction of take-over requests(Bazilinskyy et al.,2013).In the above studies it is not always clear how participants were taught to use the automated system.Zeeb et al.(2016)used a traditional approach that provided the participants with a description of the system,the functional boundaries and the alert notifications.In the vehicle,participants were also instructed to activate and deactivate the automated driving system.This approach could not be adapted to the real case because it does not ensure the correct acquisition of knowledge;thus,the drivers would not be sufficiently skilled to safely respond to a take-over request.In other studies participants could freely practice in the high-end driving simulator before the actual test drive(Gold et al.,2013).This solution would not be feasible in terms of costs,space and maintenance because it would require every car dealership to be equipped with a simulator.A lighter VR system,such as the one proposed in this paper,could instead be more easily deployed and used for training purposes at a much lower cost.Payre et al.(2017)addressed the problem of drivers training in an automated car by comparing two types of training:a simple training based only on practice in a driving simulator and an elaborated training which included a text,a tutorial video and a more elaborated practice in the simulator.They found that participants in the elaborated training group trusted more the automated driving and were able to take-over faster than those in the simple training group.Automated car research also has relevance in the field of aviation(Stanton and Marsden,1996),and in particular in studies concerning flight simulation for pilot training(Vince,1993).Although this kind of training is targeted towards professionals,important findings from this research include the occurrence of positive transfer and the fact that abstracted rendering simulators allow people to learn better than with the real thing(Stappers et al.,2003).Pilots trained on a simulator are thus able to co-pilot a craft immediately after their simulation training(Vince,1993).However,it is crucial that the training practices allow for the generalization of the skills acquired in the virtual environment and not only for an application of the rote-memorized skills specific to the training situation(Casner et al.,2013).The considerable findings from aviation and the intense scientific production in recent years suggest that the transition of control in automated cars is a valuable research topic worth investigating from the design stage to the final implementation of the new systems.Moreover,the compelling need and interest of the car industry to train a large amount of people in a reliable and cost-effective way,without compromising security,make light Virtual Reality system tools a promising solution for this purpose.2.Methods This study contained two parts:training and test drive.The aim of the training was to introduce the principles of the Level-3 Automated Driving System(ADS)-equipped vehicle,present the novel HumanMachine Interface(HMI),help the drivers to localize the HMI in the vehicle,