Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study

• URL: http://arxiv.org/abs/2407.13775v1
• Date: Sat, 29 Jun 2024 23:21:42 GMT
• Title: Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study
• Authors: Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Cathy Wu, Katherine Driggs-Campbell,
• Abstract summary: We conduct a driving simulator study (N=16) to capture driver reactions to a Cooperative RTA system. We qualitatively analyze the sentiments of drivers towards advisory systems and discuss driver preferences for various aspects of the interaction. We comment on how the advice should be communicated, the effects of the advice on driver trust, and how drivers adapt to the system.
• Score: 12.010221998198423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time Advisory (RTA) systems, such as navigational and eco-driving assistants, are becoming increasingly ubiquitous in vehicles due to their benefits for users and society. Until autonomous vehicles mature, such advisory systems will continue to expand their ability to cooperate with drivers, enabling safer and more eco-friendly driving practices while improving user experience. However, the interactions between these systems and drivers have not been studied extensively. To this end, we conduct a driving simulator study (N=16) to capture driver reactions to a Cooperative RTA system. Through a case study with a congestion mitigation assistant, we qualitatively analyze the sentiments of drivers towards advisory systems and discuss driver preferences for various aspects of the interaction. We comment on how the advice should be communicated, the effects of the advice on driver trust, and how drivers adapt to the system. We present recommendations to inform the future design of Cooperative RTA systems.

Related papers

• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• Cooperative Advisory Residual Policies for Congestion Mitigation [11.33450610735004]
  We develop a class of learned residual policies that can be used in cooperative advisory systems. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors. Our approaches successfully mitigate congestion while adapting to different driver behaviors.
  arXiv  Detail & Related papers  (2024-06-30T01:10:13Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• Beyond Voice Assistants: Exploring Advantages and Risks of an In-Car Social Robot in Real Driving Scenarios [9.297163816243282]
  We aim to answer the question: How does the presence of a social robot impact user experience in real driving scenarios? Our study begins with a user survey to understand perspectives on in-car VAs and their influence on driving experiences. We then conduct non-driving and on-road experiments with selected participants to assess user experiences with an RA.
  arXiv  Detail & Related papers  (2024-02-19T05:44:00Z)
• PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems [12.010221998198423]
  Piecewise Constant (PC) Policies address issues by structurally modeling the likeness of human driving to reduce traffic congestion. We develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP. We show that our approach successfully mitigates congestion while adapting to different driver behaviors, with 4 to 22% improvement in average speed over baselines.
  arXiv  Detail & Related papers  (2023-08-01T22:25:40Z)
• Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning [1.4699455652461728]
  We propose a warning system that uses human states in the form of driver errors. The system consists of a behavior planner Risk Maps which directly changes its prediction of the surrounding driving situation. In different simulations of a dynamic lane change and intersection scenarios, we show how the driver's behavior plan can become unsafe.
  arXiv  Detail & Related papers  (2023-06-06T16:39:58Z)
• Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
  Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving. New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories. We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
  arXiv  Detail & Related papers  (2023-04-23T16:01:36Z)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [75.83518507463226]
  Nonobjective driving experience is difficult to model. In this paper, we propose a FeedBack Loop Network (FBLNet) which attempts to model the driving experience accumulation procedure. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Human-Vehicle Cooperative Visual Perception for Shared Autonomous Driving [9.537146822132904]
  This paper proposes a human-vehicle cooperative visual perception method to enhance the visual perception ability of shared autonomous driving. Based on transfer learning, the mAP of object detection reaches 75.52% and lays a solid foundation for visual fusion. This study pioneers a cooperative visual perception solution for shared autonomous driving and experiments in real-world complex traffic conflict scenarios.
  arXiv  Detail & Related papers  (2021-12-17T03:17:05Z)
• Intelligent Roundabout Insertion using Deep Reinforcement Learning [68.8204255655161]
  We present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver.
  arXiv  Detail & Related papers  (2020-01-03T11:16:41Z)

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.