Autonomous Vehicles (AV) and Advanced Driver Assistant Systems (ADAS) prioritize safety over comfort. The intertwining factors of safety and comfort emerge as pivotal elements in ensuring the effectiveness of Autonomous Driving (AD). Users often experience discomfort when AV or ADAS drive the vehicle on their behalf. Providing a personalized human-like AD experience, tailored to match users' unique driving styles while adhering to safety prerequisites, presents a significant opportunity to boost the acceptance of AVs. This paper proposes a novel approach, Neural Driving Style Transfer (NDST), inspired by Neural Style Transfer (NST), to address this issue. NDST integrates a Personalized Block (PB) into the conventional Baseline Driving Model (BDM), allowing for the transfer of a user's unique driving style while adhering to safety parameters. The PB serves as a self-configuring system, learning and adapting to an individual's driving behavior without requiring modifications to the BDM. This approach enables the personalization of AV models, aligning the driving style more closely with user preferences while ensuring baseline safety critical actuation. Two contrasting driving styles (Style A and Style B) were used to validate the proposed NDST methodology, demonstrating its efficacy in transferring personal driving styles to the AV system. Our work highlights the potential of NDST to enhance user comfort in AVs by providing a personalized and familiar driving experience. The findings affirm the feasibility of integrating NDST into existing AV frameworks to bridge the gap between safety and individualized driving styles, promoting wider acceptance and improved user experiences.
Volkswagen AG（大众汽车公司）；Technische Universität Darmstadt（达姆施塔特工业大学）
General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement …
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…
Volkswagen AG（大众汽车公司）；Technische Universität Darmstadt（达姆施塔特工业大学）
Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a comp…
From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tianjin University（天津大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
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）
University of Science and Technology of China（中国科学技术大学）；Carnegie Mellon University（卡内基梅隆大学）
In order to operate safely on the road, autonomous vehicles need not only to be able to identify objects in front of them, but also to be able to estimate the risk level of the object in front of the vehicle automatically. It is obvious that different objects have different levels of danger to autonomous vehicles. An evaluation system is needed to automatically determine the danger level of the object for the autonomous vehicle. It would be too subjective and incomplete if the system were completely defined by humans. Based on this, we propose a framework based on nonparametric Bayesian learni…
Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study
Technical University Berlin（柏林工业大学）；University of Applied Science Kempten（肯普滕应用科学大学）；Institute for Driver Assistance and Connected Mobility（驾驶员辅助与互联移动研究所）
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…
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））
Autonomous driving research currently faces data sparsity in representation of risky scenarios. Such data is both difficult to obtain ethically in the real world, and unreliable to obtain via simulation. Recent advances in virtual reality (VR) driving simulators lower barriers to tackling this problem in simulation. We propose the first data collection framework for risky scenario driving data from real humans using VR, as well as accompanying numerical driving personality characterizations. We validate the resulting dataset with statistical analyses and model driving behavior with an eight-fa…
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…
D$^3$-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
Intelligent Transportation Systems Research Center, Wuhan University of Technology（武汉理工大学智能交通系统研究中心）；School of Mechanical and Electronic Engineering, Wuhan University of Technology（武汉理工大学机械电子工程学院）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；Hubei Key Laboratory of Distributed System Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（湖北省分布式系统安全重点实验室，华中科技大学网络空间安全学院）
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）
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…
LCSim: A Large-Scale Controllable Traffic Simulator
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Shanghai Lingang Senseauto Intelligent Technology Co., Ltd.（上海临港智己智能科技有限公司）
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…
Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment systems have not been widely studied. Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving. First, the proposed system utilizes available contextual information, such as visible cars and pedestrians, to estimate pedestrian emergence probabilities in occluded regions. These probabilities are then used in a risk assessment framework, and incorporated into…
CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous Driving
School of Mechanical and Electronic Engineering, Wuhan University of Technology（武汉理工大学机械与电子工程学院）；Intelligent Transportation Systems Research Center, Wuhan University of Technology（武汉理工大学智能交通系统研究所）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Mechanical Engineering, Southeast University（东南大学机械工程学院）
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 …
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Tsinghua University（清华大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
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…
Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles
School of Mechanical Engineering, Shandong Huayu University of Technology（山东华宇科技大学机械工程学院）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；Computer science with the University of Minnesota - Twin Cities（明尼苏达大学双城分校计算机科学）；Boston University（波士顿大学）；Faculty of Computer Science and Information Technology, University of Malaya（马来亚大学计算机科学与信息技术学院）；School of Computer Science and Technology, Taiyuan Normal University（太原师范学院计算机科学与技术学院）；College of Mechanical Engineering, Chongqing University of Technology（重庆理工大学机械工程学院）
Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives, inhibiting the transfer of learning agents to real-world contexts. We introduce a new environment, where agents Learn-to-Race (L2R) in simulated competition-style racing, using multimodal information--from virtual cameras to a comprehensive array of inertial me…
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 …
Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model
State Key Laboratory of Intelligent Transportation System, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, School of Transportation Science and Engineering, Beihang University（北京航空航天大学交通科学与工程学院，智能交通系统国家重点实验室，特种车辆自主运输技术重点实验室（工业和信息化部））；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所，多模态人工智能系统国家重点实验室）
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…
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动学院）；McGill University（麦吉尔大学）；Automotive and Robotics, Xiaomi Corporation（小米公司汽车与机器人部门）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Impact of Level 2/3 Automated Driving Technology on Road Work Zone Safety
Faculty of Railway Transportation, Shanghai Institute of Technology（上海应用技术大学轨道交通学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；Ulm University（乌尔姆大学）；University of Siegen（锡根大学）
By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely. Vehicle passengers, however, perceive a notable difference between non-experienced and experienced drivers. In particular, they may get the impression that the latter ones anticipate what will happen in the next few moments and consider these foresights in their driving behavior. To make the driving style of automated vehicles comparable to the one of human drivers with respect to comfort and perceived safety, the aforementioned anticipation skills need to become a built-in f…
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
Shanghai Jiao Tong University（上海交通大学）；Cowa Robot（科沃机器人）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems, however, it has been found that DNNs are vulnerable to adversarial examples. For such reason, adversarial perturbations have been recently studied in several respects. However, most previous works have focused on image classification tasks, and it has never been studied regarding adversarial perturbations on Image-to-image (Im2Im) translation tasks, showing great success in handling paired and/or unpaired mapping problems in the field of autonomous driving and robotics. This paper examines di…
Long-Horizon Consistent and Interaction-Aware World Models for Multi-Style End-to-End Driving
School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）智能科学与工程学院）；Autonomous Driving Center, Shanghai Utopilot Technology Co.Ltd.（上海元戎智能科技有限公司自动驾驶中心）
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…
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）
