Don't Drive Me My Way

Lehsing, Christian; Jünger, Lukas; Bengler, Klaus

doi:10.1145/3368926.3369692

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Some literature found that when a driver uses ADAS, the system characteristic he prefers may not be completely consistent with his own naturalistic driving characteristic. Some literature [ 13 ] compared the braking process of a driver during naturalistic driving and an autonomous driving vehicle when approaching a zebra crossing. It was observed that if the system characteristic was aligned with this driver’s naturalistic driving style, such a characteristic was often not rated as the safest and most cooperative by this driver.…”

Section: Introductionmentioning

confidence: 99%

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

Chen,

Lan

et al. 2024

Sensors

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To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver’s preference is consistent with this driver’s naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers’ real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers’ naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers’ real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers.

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Section: Introductionmentioning

confidence: 99%

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

Chen,

Lan

et al. 2024

Sensors

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“…One path to improve user acceptance is to allow personalization so that the automated vehicle’s driving style matches the driver’s preferred way of manual driving (Bellem et al, 2018; Hasenjäger & Wersing, 2017; Paschalidis et al, 2020; Sun et al, 2020; Trende et al, 2019). Research exploring if drivers prefer driving automation that emulates their driving style shows mixed results (Basu et al, 2017; Griesche et al, 2016; Lehsing et al, 2019). However, these explorations rely on the driver’s subjective assessment of whether the driving style is like their own.…”

Section: Introductionmentioning

confidence: 99%

Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity

et al. 2023

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Objective This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. Background Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent’s actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. Methods A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. Results Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants’ approach to the intersection and not their departure. Conclusion Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver’s perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

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Towards Human-Compatible Autonomous Car: A Study of Non-Verbal Turing Test in Automated Driving With Affective Transition Modelling

Jiang

et al. 2024

IEEE Trans. Affective Comput.

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We tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition, indicating an important role of affective transition in passengers' ascription of humanness.

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Don't Drive Me My Way

Cited by 5 publications

References 21 publications

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity

Towards Human-Compatible Autonomous Car: A Study of Non-Verbal Turing Test in Automated Driving With Affective Transition Modelling

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