Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving

Delmas, Maxime; Camps, Valérie; Lemercier, Céline

doi:10.1016/j.aap.2024.107512

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2024

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Cited by 3 publications

References 19 publications

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A journey from users' experience to their expectations in the realm of future advanced driver assistance systems

Patel,

Monteiro,

Bicho

2024

Transportation Planning and Technology

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A journey from users' experience to their expectations in the realm of future advanced driver assistance systems

Patel,

Monteiro,

Bicho

2024

Transportation Planning and Technology

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Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study

Haselberger,

Böhle,

Schick

et al. 2024

Preprint

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A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations

Xia,

Chen

2024

Sensors

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Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

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Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving

Cited by 3 publications

References 19 publications

A journey from users' experience to their expectations in the realm of future advanced driver assistance systems

A journey from users' experience to their expectations in the realm of future advanced driver assistance systems

Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations

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