1、虚拟现实与汽车自动驾驶外文翻译中英文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 RealityDaniele Sportillo, Alexis Paljic, Luciano OjedaAbstractIn conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to
2、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
3、 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 ad
4、dition, 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.
5、 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 drivi
6、ng 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 r
7、ole 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, Training1.IntroductionImagine you are reading this article in your car as you drive on
8、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 contr
9、ol, 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-3S. International (2017) do not require drivers to constantly monitor their driving environment; t
10、hey 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 t
11、he 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 ha
12、ve 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
13、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
14、 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 ass
15、essment 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
16、controllabilityandreproducibilityof 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. Howeve
17、r, 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 oflightVirtual Reality systems, in particular, for the acquisition of skills for
18、 the Transfer of Control (ToC) in highly automated cars. By using the adjectivelight, 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 th
19、anks to the portability and the cost-effectiveness,lightVR 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
20、 aims to compare the effectiveness of a training program based on this system with a user manual and with a fixed-basedriving 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 q
21、uestionnaires.1.1.Related workVirtual 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 possibl
22、e 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
23、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
24、 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
25、-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
26、 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 initialresumptionof 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 sta
27、tic 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 a
28、nalyze 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 controller
29、s 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
30、 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 andhazardous situationswhich are ethically not possible to evaluate on real roads (I
31、hemedu-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 b
32、ehavioral 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, r
33、esearchers 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 wh
