Recently, LLM-powered driver agents have demonstrated considerable potential in the field of autonomous driving, showcasing human-like reasoning and decision-making abilities.However, current research on aligning driver agent behaviors with human driving styles remains limited, partly due to the scarcity of high-quality natural language data from human driving behaviors.To address this research gap, we propose a multi-alignment framework designed to align driver agents with human driving styles through demonstrations and feedback. Notably, we construct a natural language dataset of human driver behaviors through naturalistic driving experiments and post-driving interviews, offering high-quality human demonstrations for LLM alignment. The framework's effectiveness is validated through simulation experiments in the CARLA urban traffic simulator and further corroborated by human evaluations. Our research offers valuable insights into designing driving agents with diverse driving styles.The implementation of the framework and details of the dataset can be found at the link.
Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural norms. It is known that socially aware AVs can be designed if there exist a mechanism to understand the behaviors of human drivers. We present a notion of Machine Theory of Mind (M-ToM) to infer the behaviors of human drivers by observing the trajectory of their vehicles. Our M-ToM approach, called StylePredict, is based on trajectory analysis of vehicles, which has been investigated in robotics and computer vision. S…
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）；Hino Motors, Ltd.（日野汽车公司）
Lane change is a very demanding driving task and number of traffic accidents are induced by mistaken maneuvers. An automated lane change system has the potential to reduce driver workload and to improve driving safety. One challenge is how to improve driver acceptance on the automated system. From the viewpoint of human factors, an automated system with different styles would improve user acceptance as the drivers can adapt the style to different driving situations. This paper proposes a method to design different lane change styles in automated driving by analysis and modeling of truck driver…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose an unsupervised method for inferring driver traits such as driving styles from observed vehicle trajectories. We use a variational autoencoder with recurrent neural networks to learn a latent representation of traits without any ground truth trait labels. Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate …
A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness
City University of Hong Kong（香港城市大学）；Zhejiang University of Technology（浙江工业大学）；University of Victoria（维多利亚大学）；The University of Queensland（昆士兰大学）；University of Puerto Rico（波多黎各大学）
We present a new measure, CMetric, to classify driver behaviors using centrality functions. Our formulation combines concepts from computational graph theory and social traffic psychology to quantify and classify the behavior of human drivers. CMetric is used to compute the probability of a vehicle executing a driving style, as well as the intensity used to execute the style. Our approach is designed for realtime autonomous driving applications, where the trajectory of each vehicle or road-agent is extracted from a video. We compute a dynamic geometric graph (DGG) based on the positions and pr…
University of Oxford（牛津大学）；Umeå University（于默奥大学）；University of Nottingham（诺丁汉大学）；Oxford Robotics Institute（牛津机器人研究所）
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, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While…
Toyota Research Institute（丰田研究院）；Georgia Institute of Technology（佐治亚理工学院）
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. To investigate the impact of driving style on user acceptance, we 1) develop a data-driven approach to personalize driving style and 2) demonstrate that personalization significantly impacts attitudes towards AVs. Our approach learns a high-level model that tunes low-level controllers to ensure safe and personalized c…
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
Intelligent Transportation Thrust, Systems Hub, and Center of Seamless Connectivity & Connected Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（智能交通 thrust、系统中心及无缝连接与智能连接研究院，香港科学与技术大学（广州））
We present a new method for multi-agent planning involving human drivers and autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging. In multi-agent planning, the main challenge is to predict the actions of other agents, especially human drivers, as their intentions are hidden from other agents. Our algorithm uses game theory to develop a new auction, called GamePlan, that directly determines the optimal action for each agent based on their driving style (which is observable via commonly available sensors). GamePlan assigns a higher priority to more aggressive or…
This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose an Occupant's Preference Metric (OPM) which is defining a preferred lateral and longitudinal acceleration region with maximum allowable jerk for users. Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles. The proposed system not only provides the criteri…
Politecnico Di Milano（米兰理工大学）；Xidian University（西安电子科技大学）
With the implementation of reinforcement learning (RL) algorithms, current state-of-art autonomous vehicle technology have the potential to get closer to full automation. However, most of the applications have been limited to game domains or discrete action space which are far from the real world driving. Moreover, it is very tough to tune the parameters of reward mechanism since the driving styles vary a lot among the different users. For instance, an aggressive driver may prefer driving with high acceleration whereas some conservative drivers prefer a safer driving style. Therefore, we propo…
STAGE: STyle-controllable Action GEneration for personalized autonomous driving
Northwestern Polytechnical University（西北工业大学）；School of Astronautics（航天学院）；Research Center for Intelligent Robotics（智能机器人研究中心）；National Key Laboratory of Aerospace Flight Dynamics（航空航天飞行动力学重点实验室）
Towards the "Digital Me": A vision of authentic Conversational Agents powered by personal Human Digital Twins
Technical University of Denmark（丹麦技术大学）；School of Design（设计学院）；University of Strathclyde（斯特拉斯克莱德大学）；Department of Design, Manufacturing and Engineering Management（设计、制造与工程管理系）
Self-Perception Versus Objective Driving Behavior: Subject Study of Lateral Vehicle Guidance
Institute for Driver Assistance and Connected Mobility, University of Applied Science Kempten（肯普滕应用科学大学驾驶辅助与互联移动研究所）；Department of Automotive Engineering, Technical University Berlin（柏林工业大学汽车工程系）
A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant
Simulation has long been an essential part of testing autonomous driving systems, but only recently has simulation been useful for building and training self-driving vehicles. Vehicle behavioural models are necessary to simulate the interactions between robot cars. This paper proposed a new method to formalize the lane-changing model in urban driving scenarios. We define human incentives from different perspectives, speed incentive, route change incentive, comfort incentive and courtesy incentive etc. We applied a decision-theoretical tool, called Multi-Criteria Decision Making (MCDM) to take …
Kettering University（凯特林大学）；University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）
This paper illustrates the MIR (Mobile Intelligent Robotics) Vehicle: a feasible option of transforming an electric ride-on-car into a modular Graphics Processing Unit (GPU) powered autonomous platform equipped with the capability that supports test and deployment of various intelligent autonomous vehicles algorithms. To use a platform for research, two components must be provided: perception and control. The sensors such as incremental encoders, an Inertial Measurement Unit (IMU), a camera, and a LIght Detection And Ranging (LIDAR) must be able to be installed on the platform to add the capab…
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航天工程学院）
Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human-like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for …
Biasing the Driving Style of an Artificial Race Driver for Online Time-Optimal Maneuver Planning
Department of Industrial Engineering, University of Trento（工业工程系，特伦托大学）；Department of Electrical and Information Engineering, Politecnico di Bari（电气与信息工程系，巴里理工学院）；Professorship of Autonomous Vehicle Systems, Technical University of Munich（自主车辆系统教授职位，慕尼黑技术大学）
RePCS: Diagnosing Data Memorization in LLM-Powered Retrieval-Augmented Generation
1 Institute of Information Technology, Vietnam Academy of Science；2 Institute of Information Technology, Vietnam Academy of Science；3 Department of Mathematics, Computer Science \& Physics, Rockford University, Illinois, United States Email；4 Department of Information Technology, Dong A University, Da Nang, Vietnam Email
With progress in enabling autonomous cars to drive safely on the road, it is time to start asking how they should be driving. A common answer is that they should be adopting their users' driving style. This makes the assumption that users want their autonomous cars to drive like they drive - aggressive drivers want aggressive cars, defensive drivers want defensive cars. In this paper, we put that assumption to the test. We find that users tend to prefer a significantly more defensive driving style than their own. Interestingly, they prefer the style they think is their own, even though their a…
BMW AG（宝马公司）；Graf Engineering（格拉夫工程公司）；University of Salzburg（萨尔茨堡大学）
We provide an ice friction model for vehicle dynamics of a two-man bobsled which can be used for driver evaluation and in a driver-in-the-loop simulator. Longitudinal friction is modeled by combining experimental results with finite element simulations to yield a correlation between contact pressure and friction. To model lateral friction, we collect data from 44 bobsleigh runs using special sensors. Non-linear regression is used to fit a bob-specific one-track vehicle dynamics model to the data. It is applied in driving simulation and enables a novel method for bob driver evaluation. Bob driv…
To develop driving automation technologies for human, a human-centered methodology should be adopted for ensured safety and satisfactory user experience. Automated lane change decision in dense highway traffic is challenging, especially when considering the personalized preferences of different drivers. To fulfill human driver centered decision algorithm development, we carry out driver-in-the-loop experiments on a 6-Degree-of-Freedom driving simulator. Based on the analysis of the lane change data by drivers of three specific styles,personalization indicators are selected to describe the driv…
Technische Universität Darmstadt（达姆施塔特工业大学）；Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Max-Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules. A global target trajectory distribution is learned with Probabilistic Movement Primitives, clothoids are utilized for local path generation, and t…
Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver
Centre for Applied Autonomous Sensor Systems (AASS)（应用自主传感器系统中心（AASS））；Örebro University（厄勒布鲁大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；University of Skövde（舍夫德大学）；TU Munich（慕尼黑工业大学）
Institute of Power Machinery and Vehicular Engineering, Faculty of Engineering, Zhejiang University（浙江大学工学部动力机械及车辆工程研究所）
Highway pilot assist has become the front line of competition in advanced driver assistance systems. The increasing requirements on safety and user acceptance are calling for personalization in the development process of such systems. Inspired by a finding on drivers' car-following preferences on lateral direction, a personalized highway pilot assist algorithm is proposed, which consists of an Intelligent Driver Model (IDM) based speed control model and a novel lane-keeping model considering the leading vehicle's lateral movement. A simulated driving experiment is conducted to analyse driver g…
