Developing Situational Awareness for Joint Action with Autonomous Vehicles
- URL: http://arxiv.org/abs/2404.11800v1
- Date: Wed, 17 Apr 2024 23:41:48 GMT
- Title: Developing Situational Awareness for Joint Action with Autonomous Vehicles
- Authors: Robert Kaufman, David Kirsh, Nadir Weibel,
- Abstract summary: To achieve joint human-AV action goals, sufficient situational awareness must be held by the human, AV, and human-AV system collectively.
We present a systems-level framework that integrates cognitive theories of joint action and situational awareness as a means to tailor communications.
- Score: 8.328428631388556
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unanswered questions about how human-AV interaction designers can support rider's informational needs hinders Autonomous Vehicles (AV) adoption. To achieve joint human-AV action goals - such as safe transportation, trust, or learning from an AV - sufficient situational awareness must be held by the human, AV, and human-AV system collectively. We present a systems-level framework that integrates cognitive theories of joint action and situational awareness as a means to tailor communications that meet the criteria necessary for goal success. This framework is based on four components of the shared situation: AV traits, action goals, subject-specific traits and states, and the situated driving context. AV communications should be tailored to these factors and be sensitive when they change. This framework can be useful for understanding individual, shared, and distributed human-AV situational awareness and designing for future AV communications that meet the informational needs and goals of diverse groups and in diverse driving contexts.
Related papers
- Improving Human-Autonomous Vehicle Interaction in Complex Systems [0.9790236766474201]
I argue that understanding the relationships between different aspects of the human-AV system can help us build improved and adaptable AV communications.
Findings highlight the need for task-sensitive, modality-appropriate communications tuned to learner cognitive limits and goals.
Third, I use machine learning (ML) to illuminate personal factors predicting trust in AVs.
arXiv Detail & Related papers (2025-04-24T01:09:51Z) - Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework [8.077621888442337]
Automated Vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility.
Transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs)
Ensuring AVs' compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic.
This study carries out the first comprehensive scoping review to assess the current state of the art in developing SCAVs.
arXiv Detail & Related papers (2025-01-10T16:39:01Z) - Characterizing Behavioral Differences and Adaptations of Automated Vehicles and Human Drivers at Unsignalized Intersections: Insights from Waymo and Lyft Open Datasets [9.080817016043769]
The integration of autonomous vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency.
This study aims to bridge the gap by examining behavioral differences and adaptations of AVs and human-driven vehicles (HVs) at unsignalized intersections.
Using a systematic methodology, the research identifies and analyzes merging and crossing conflicts by calculating key safety and efficiency metrics.
arXiv Detail & Related papers (2024-10-16T13:19:32Z) - Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap [4.2330023661329355]
This study presents a review to discuss the complexities associated with explanation generation and presentation.
Our roadmap is underpinned by principles of responsible research and innovation.
By exploring these research directions, the study aims to guide the development and deployment of explainable AVs.
arXiv Detail & Related papers (2024-03-19T11:43:41Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - Development and Assessment of Autonomous Vehicles in Both Fully
Automated and Mixed Traffic Conditions [0.0]
The paper presents a multi-stage approach, starting with the development of a single AV and progressing to connected AVs.
A survey is conducted to validate the driving performance of the AV and will be utilized for a mixed traffic case study.
Results show that using deep reinforcement learning, the AV acquired driving behavior that reached human driving performance.
The adoption of sharing and caring based V2V communication within AV networks enhances their driving behavior, aids in more effective action planning, and promotes collaborative behavior amongst the AVs.
arXiv Detail & Related papers (2023-12-08T02:40:11Z) - Convergence of Communications, Control, and Machine Learning for Secure
and Autonomous Vehicle Navigation [78.60496411542549]
Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations.
This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
arXiv Detail & Related papers (2023-07-05T21:38:36Z) - Sociotechnical Specification for the Broader Impacts of Autonomous
Vehicles [10.08754310662559]
Autonomous Vehicles (AVs) will have a transformative impact on society.
The ability to control both the individual behavior of AVs and the overall flow of traffic provides new affordances that permit AVs to control these effects.
This paper presents a problem of sociotechnical specification: the need to distinguish which essential features of the transportation system are in or out of scope for AV development.
arXiv Detail & Related papers (2022-05-15T23:03:43Z) - Attention Based Communication and Control for Multi-UAV Path Planning [48.389498274216926]
This letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning.
The ISHA mechanism is run by a communication helper collecting the state embeddings of UAVs and distributing an attention score vector to each UAV.
The attention scores computed by ISHA identify how many interactions with other UAVs should be considered in each UAV's control decision-making.
arXiv Detail & Related papers (2021-12-20T03:11:46Z) - Interpretation of Emergent Communication in Heterogeneous Collaborative
Embodied Agents [83.52684405389445]
We introduce the collaborative multi-object navigation task CoMON.
In this task, an oracle agent has detailed environment information in the form of a map.
It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals.
We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
arXiv Detail & Related papers (2021-10-12T06:56:11Z) - Transferable Deep Reinforcement Learning Framework for Autonomous
Vehicles with Joint Radar-Data Communications [69.24726496448713]
We propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions.
We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV.
We show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.
arXiv Detail & Related papers (2021-05-28T08:45:37Z) - Towards robust sensing for Autonomous Vehicles: An adversarial
perspective [82.83630604517249]
It is of primary importance that the resulting decisions are robust to perturbations.
Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements.
A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
arXiv Detail & Related papers (2020-07-14T05:25:15Z) - A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy:
From Physics-Based to AI-Guided Driving Policy Learning [7.881140597011731]
This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control.
We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, raise open questions, and help suggest the building blocks and areas where AI could play a role in mixed autonomy.
arXiv Detail & Related papers (2020-07-10T04:27:39Z)
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.