Building Trust in Autonomous Vehicles: Role of Virtual Reality Driving Simulators in HMI Design

• URL: http://arxiv.org/abs/2007.13371v1
• Date: Mon, 27 Jul 2020 08:42:07 GMT
• Title: Building Trust in Autonomous Vehicles: Role of Virtual Reality Driving Simulators in HMI Design
• Authors: Lia Morra, Fabrizio Lamberti, F. Gabriele Prattic\'o, Salvatore La Rosa, Paolo Montuschi
• Abstract summary: We propose a methodology to validate the user experience in AVs based on continuous, objective information gathered from physiological signals. We applied this methodology to the design of a head-up display interface delivering visual cues about the vehicle's sensory and planning systems.
• Score: 8.39368916644651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The investigation of factors contributing at making humans trust Autonomous Vehicles (AVs) will play a fundamental role in the adoption of such technology. The user's ability to form a mental model of the AV, which is crucial to establish trust, depends on effective user-vehicle communication; thus, the importance of Human-Machine Interaction (HMI) is poised to increase. In this work, we propose a methodology to validate the user experience in AVs based on continuous, objective information gathered from physiological signals, while the user is immersed in a Virtual Reality-based driving simulation. We applied this methodology to the design of a head-up display interface delivering visual cues about the vehicle' sensory and planning systems. Through this approach, we obtained qualitative and quantitative evidence that a complete picture of the vehicle's surrounding, despite the higher cognitive load, is conducive to a less stressful experience. Moreover, after having been exposed to a more informative interface, users involved in the study were also more willing to test a real AV. The proposed methodology could be extended by adjusting the simulation environment, the HMI and/or the vehicle's Artificial Intelligence modules to dig into other aspects of the user experience.

Related papers

• Behavioural gap assessment of human-vehicle interaction in real and virtual reality-based scenarios in autonomous driving [7.588679613436823]
  We present a first and innovative approach to evaluating what we term the behavioural gap, a concept that captures the disparity in a participant's conduct when engaging in a VR experiment compared to an equivalent real-world situation. In the experiment, the pedestrian attempts to cross the road in the presence of different driving styles and an external Human-Machine Interface (eHMI) Results show that participants are more cautious and curious in VR, affecting their speed and decisions, and that VR interfaces significantly influence their actions.
  arXiv  Detail & Related papers  (2024-07-04T17:20:17Z)
• Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles [4.1636282808157254]
  We study the role of explainable AI and human-machine interface jointly in building trust in vehicle autonomy. We present a situation awareness framework for calibrating users' trust in self-driving behavior.
  arXiv  Detail & Related papers  (2024-04-10T23:02:13Z)
• A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving [21.130543517747995]
  This paper introduces the Human-Like Trajectory Prediction (H) model, which adopts a teacher-student knowledge distillation framework. The "teacher" model mimics the visual processing of the human brain, particularly the functions of the occipital and temporal lobes. The "student" model focuses on real-time interaction and decision-making, capturing essential perceptual cues for accurate prediction.
  arXiv  Detail & Related papers  (2024-02-29T15:22:26Z)
• Reinforcement Learning with Human Feedback for Realistic Traffic Simulation [53.85002640149283]
  Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge. This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
  arXiv  Detail & Related papers  (2023-09-01T19:29:53Z)
• Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses [130.15554653948897]
  In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome. Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly. We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
  arXiv  Detail & Related papers  (2023-02-16T16:54:10Z)
• VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles [131.2240621036954]
  We present VISTA, an open source, data-driven simulator that integrates multiple types of sensors for autonomous vehicles. Using high fidelity, real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, and event-based cameras. We demonstrate the ability to train and test perception-to-control policies across each of the sensor types and showcase the power of this approach via deployment on a full scale autonomous vehicle.
  arXiv  Detail & Related papers  (2021-11-23T18:58:10Z)
• Game and Simulation Design for Studying Pedestrian-Automated Vehicle Interactions [1.3764085113103217]
  We first present contemporary tools in the field and then propose the design and development of a new application that facilitates pedestrian point of view research. We conduct a three-step user experience experiment where participants answer questions before and after using the application in various scenarios.
  arXiv  Detail & Related papers  (2021-09-30T15:26:18Z)
• Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images [58.287120077778205]
  We introduce a framework for analyzing robustness of the learning algorithm w.r.t varying quality in the image input for autonomous driving. Using the results of sensitivity analysis, we propose an algorithm to improve the overall performance of the task of "learning to steer"
  arXiv  Detail & Related papers  (2021-02-26T02:08:07Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection [15.36267013724161]
  We propose a visual analytics system, VATLD, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications. The disentangled representation learning extracts data semantics to augment human cognition with human-friendly visual summarization. We also demonstrate the effectiveness of various performance improvement strategies with our visual analytics system, VATLD, and illustrate some practical implications for safety-critical applications in autonomous driving.
  arXiv  Detail & Related papers  (2020-09-27T22:39:00Z)
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [88.0416857308144]
  We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
  arXiv  Detail & Related papers  (2020-08-13T17:20:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.