Being good (at driving): Characterizing behavioral expectations on automated and human driven vehicles

• URL: http://arxiv.org/abs/2502.08121v1
• Date: Wed, 12 Feb 2025 04:59:20 GMT
• Title: Being good (at driving): Characterizing behavioral expectations on automated and human driven vehicles
• Abstract summary: We put forth the concept of Drivership as a framing for the realization of good driving behaviors.<n>Drivership grounds the evaluation of driving behaviors in the alignment between the mutualistic expectations that exist amongst road users.
• Score: 0.4476047439400351
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: For over a century, researchers have wrestled with how to define good driving behavior, and the debate has surfaced anew for automated vehicles (AVs). We put forth the concept of Drivership as a framing for the realization of good driving behaviors. Drivership grounds the evaluation of driving behaviors in the alignment between the mutualistic expectations that exist amongst road users. Leveraging existing literature, we distinguish Empirical Expectations (i.e., reflecting "beliefs that a certain behavior will be followed," drawing on past experiences) (Bicchieri, 2006); and Normative Expectations (i.e., reflecting "beliefs that a certain behavior ought to be followed," based on societally agreed-upon principles) (Bicchieri, 2006). Because societal expectations naturally shift over time, we introduce a third type of expectation, Furtherance Expectations, denoting behavior which could be exhibited to enable continuous improvement of the transportation ecosystem. We position Drivership within the space of societal Normative Expectations, which may overlap with some Empirical and Furtherance Expectations, constrained by what is technologically and physically feasible. Additionally, we establish a novel vocabulary to rigorously tackle conversations on stakeholders' expectations, a key feature of value-sensitive design. We also detail how Drivership comprises safety-centric behaviors and what we term socially-aware behaviors (where there are no clear safety stakes). Drivership supports multiple purposes, including advancing the understanding and evaluation of driving performance through benchmarking based on many criteria. As such, we argue that an appropriate framing of the notion of Drivership also underpins the overall development of a safety case. The paper explores these applications under the more general tenet of Drivership as a central element to roadway citizenship.

Related papers

• DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models [53.73898294218455]
  DriveJudge is a driving evaluation agent that combines rule-grounded evaluation with Vision-Language Model (VLM) reasoning.<n>We introduce two benchmark tasks: Driving Quality Classification and Trajectory Preference Selection.<n>DriveJudge outperformsS for driving quality classification by 21.23 AUC, and the recent VLM-based DriveCritic for trajectory preference selection by 6.5%.
  arXiv  Detail & Related papers  (2026-06-15T23:39:36Z)
• A User-driven Design Framework for Robotaxi [12.910122661259718]
  We investigate real-world robotaxi use through 18 semi-structured interviews and autoethnographic ride experiences.<n>We found that users were drawn to robotaxis by low cost, social recommendation, and curiosity.<n>They valued a distinctive set of benefits, such as an increased sense of agency, and consistent driving behavioral consistency.
  arXiv  Detail & Related papers  (2026-02-22T09:33:18Z)
• Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights [18.92479778025183]
  In driving scenarios, a vehicle's trajectory is determined by the decision-making process of human drivers. Previous models fail to capture the true intentions of human drivers, leading to suboptimal performance in long-term trajectory prediction. We introduce a Cognitive-Informed Transformer (CITF) that incorporates a cognitive concept, Perceived Safety, to interpret drivers' decision-making mechanisms.
  arXiv  Detail & Related papers  (2025-02-27T13:43:17Z)
• 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)
• The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review [43.30610493968783]
  We review state-of-the-art deep learning-based planning systems, and focus on how they integrate prediction. We discuss the implications, strengths, and limitations of different integration principles.
  arXiv  Detail & Related papers  (2023-08-10T17:53:03Z)
• 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 [50.936478241688114]
  Nonobjective driving experience is difficult to model, so a mechanism simulating the driver experience accumulation procedure is absent in existing methods. We propose a FeedBack Loop Network (FBLNet), which attempts to model the driving experience accumulation procedure. Our model exhibits a solid advantage over existing methods, achieving an outstanding performance improvement on two driver attention benchmark datasets.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• Behavioral Intention Prediction in Driving Scenes: A Survey [70.53285924851767]
  Behavioral Intention Prediction (BIP) simulates a human consideration process and fulfills the early prediction of specific behaviors. This work provides a comprehensive review of BIP from the available datasets, key factors and challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications.
  arXiv  Detail & Related papers  (2022-11-01T11:07:37Z)
• From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions [10.557942353553859]
  In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions.
  arXiv  Detail & Related papers  (2022-04-19T19:39:13Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Evaluating Robustness over High Level Driving Instruction for Autonomous Driving [5.180648702293017]
  In recent years, we have witnessed increasingly high performance in the field of autonomous end-to-end driving. We propose here an evaluation method, namely a benchmark that allows to assess the robustness of an agent.
  arXiv  Detail & Related papers  (2021-05-20T20:10:14Z)
• Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models [82.34305824719101]
  Humans have a remarkable ability to make decisions by accurately reasoning about future events. We develop a general-purpose contingency planner that is learned end-to-end using high-dimensional scene observations. We show how this model can tractably learn contingencies from behavioral observations.
  arXiv  Detail & Related papers  (2021-04-21T14:30:20Z)
• Emergent Road Rules In Multi-Agent Driving Environments [84.82583370858391]
  We analyze what ingredients in driving environments cause the emergence of road rules. We find that two crucial factors are noisy perception and agents' spatial density. Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.
  arXiv  Detail & Related papers  (2020-11-21T09:43:50Z)

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.