STAGE: STyle-controllable Action GEneration for personalized autonomous driving

• URL: http://arxiv.org/abs/2607.29517v1
• Date: Fri, 31 Jul 2026 15:17:50 GMT
• Title: STAGE: STyle-controllable Action GEneration for personalized autonomous driving
• Abstract summary: Driving style refers to the behavioral preferences that drivers maintain during driving.<n>We develop a style controllable action generation method, STAGE, for driving tasks.<n>We reveal the unique functionalities of STAGE, including its style controllability, style continuity, driving style alignment capability and driving safety.
• Score: 15.187069954986343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driving style refers to the behavioral preferences that drivers maintain during driving, shaped by their diverse experiences, habits, and needs, and is typically reflected in varying levels of aggressiveness. If humans choose to use autonomous driving systems, they would expect the driving style of the systems to closely resemble their own habit. However, this is challenging for current industrial autonomous driving systems. To address this, we developed a style controllable action generation method, STAGE, for driving tasks. Its training process is based on imitation learning, incorporating both style value and latent value action modality encoding. Preference learning is then used to identify the user's driving style as a continuous, monotonic style value. And to reduce the cost of human involvement in the preference training process, we also developed a set of rules to compare driving style in data pairs. Then, during inference, the user inputs the style value to control the generated action patterns, dynamically meeting the user's expectations. Using the STAGE method, we verified that the style-controlled action generation results in several typical road scenarios significantly align with human expectations. Furthermore, through comparisons between the STAGE method and various other approaches, we reveal the unique functionalities of STAGE, including its style controllability, style continuity, driving style alignment capability and driving safety. The code for this work is available at: https://github.com/CarlDegio/STAGE

Related papers

• Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving [18.914134572789703]
  Drive My Way (DMW) is a personalized driving framework that aligns with users' long-term driving habits and adapts to real-time user instructions.<n>DMW learns a user embedding from our personalized driving dataset collected across multiple real drivers and conditions the policy on this embedding during planning.<n>Our data and code are available at https://dmw-cvpr.io/.
  arXiv  Detail & Related papers  (2026-03-26T17:59:54Z)
• Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving [18.624545462468642]
  We propose a novel approach that leverages contrastive learning to extract a dictionary of driving styles from pre-existing human driving data.<n>Our empirical evaluation confirms that the behaviors generated by our approach are both safer and more human-like than those of the machine-learning-based baseline methods.
  arXiv  Detail & Related papers  (2025-03-07T08:26:04Z)
• MetaFollower: Adaptable Personalized Autonomous Car Following [63.90050686330677]
  We propose an adaptable personalized car-following framework - MetaFollower. We first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability.
  arXiv  Detail & Related papers  (2024-06-23T15:30:40Z)
• Situation Awareness for Driver-Centric Driving Style Adaptation [3.568617847600189]
  We propose a situation-aware driving style model based on different visual feature encoders pretrained on fleet data. Our experiments show that the proposed method outperforms static driving styles significantly and forms plausible situation clusters.
  arXiv  Detail & Related papers  (2024-03-28T17:19:16Z)
• Robust Driving Policy Learning with Guided Meta Reinforcement Learning [49.860391298275616]
  We introduce an efficient method to train diverse driving policies for social vehicles as a single meta-policy. By randomizing the interaction-based reward functions of social vehicles, we can generate diverse objectives and efficiently train the meta-policy. We propose a training strategy to enhance the robustness of the ego vehicle's driving policy using the environment where social vehicles are controlled by the learned meta-policy.
  arXiv  Detail & Related papers  (2023-07-19T17:42:36Z)
• Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation [29.623575243494475]
  Traffic simulation plays a crucial role in evaluating and improving autonomous driving planning systems. To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents. We propose a socially-controllable behavior generation model for this purpose, which allows the users to specify the level of courtesy of the generated trajectory.
  arXiv  Detail & Related papers  (2023-03-24T06:38:42Z)
• MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving [7.856998585396421]
  Personalization of autonomous vehicles (AV) may significantly increase trust, use, and acceptance. In particular, we hypothesize that the similarity of an AV's driving style compared to the end-user's driving style will have a major impact on end-user's willingness to use the AV.
  arXiv  Detail & Related papers  (2023-01-20T14:14:49Z)
• Learning to drive from a world on rails [78.28647825246472]
  We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving trajectory. Our method ranks first on the CARLA leaderboard, attaining a 25% higher driving score while using 40 times less data.
  arXiv  Detail & Related papers  (2021-05-03T05:55:30Z)
• Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
  Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing. Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints. We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
  arXiv  Detail & Related papers  (2020-09-23T14:56:19Z)
• Learning Accurate and Human-Like Driving using Semantic Maps and Attention [152.48143666881418]
  This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. We exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data.
  arXiv  Detail & Related papers  (2020-07-10T22:25:27Z)
• 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.