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 behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver model parameters and three lane change styles were classified as the aggressive, medium, and conservative ones. The proposed automated lane change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effect of different driving styles on driver experience and acceptance was evaluated. The evaluation results demonstrate that the different lane change styles could be distinguished by the drivers; meanwhile, the three styles were overall evaluated as acceptable on safety issues and reliable by the human drivers. This study provides insight into designing the automated driving system with different driving styles and the findings can be applied to commercial automated trucks.
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University（同济大学道路与交通工程教育部重点实验室）；China Railway Siyuan Survey and Design Group Co., Ltd.（中铁第四勘察设计院集团有限公司）；Institute of Logistics Science and Engineering, Shanghai Maritime University（上海海事大学物流科学与工程研究院）
The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Very few traffic conflict analysis studies comprehensively consider different vehicle types at freeway merging section. Thus, the main objective of this study is to analyse conflicts between different vehicle types at freeway merging section. Field data are collected by Unmanned Aerial Vehicle (UAV) at merging areas in Shanghai, China. Vehicle extraction method is utilized to obtain vehicle trajectories. Time-to-collision (TTC) is utilized as the surrogate safety measur…
The Autonomous Truck-Mounted Attenuator (ATMA) system is a lead-follower vehicle system based on autonomous driving and connected vehicle technologies. The lead truck performs maintenance tasks on the road, and the unmanned follower truck alerts passing vehicles about the moving work zone and protects workers and the equipment. While the ATMA has been under testing by transportation maintenance and operations agencies recently, a simulator-based testing capability is a supplement, especially if human subjects are involved. This paper aims to discover how passing drivers perceive, understand, a…
Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究所）；Virginia Tech Transportation Institute（弗吉尼亚理工大学交通研究所）
Jaguar Land Rover Limited（捷豹路虎有限公司）；University of Surrey（萨里大学）
Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with saf…
Understanding human driving behavior is crucial to develop autonomous vehicles' algorithms. However, most low level automation, such as the one in advanced driving assistance systems (ADAS), is based on objective safety measures, which are not always aligned with what the drivers perceive as safe and their correspondent driving behavior. Finding the bridge between the subjective perception and objective safety measures has been analyzed in this paper focusing specifically on lane-change scenarios. Results showed statistically significant differences between what is perceived as safe by drivers…
mercedes-Benz AG（梅赛德斯-奔驰股份公司）；Ulm University（乌尔姆大学）；Eberhard Karls University Tübingen（埃伯哈德·卡尔斯大学图宾根）
The prediction of surrounding traffic participants behavior is a crucial and challenging task for driver assistance and autonomous driving systems. Today's approaches mainly focus on modeling dynamic aspects of the traffic situation and try to predict traffic participants behavior based on this. In this article we take a first step towards extending this common practice by calculating location-specific a-priori lane change probabilities. The idea behind this is straight forward: The driving behavior of humans may vary in exactly the same traffic situation depending on the respective location. …
Dept. of Mechanical Engineering, University of New Brunswick（新 Brunswick大学机械工程系）；Faculty of Interdisciplinary Studies, University of New Brunswick（新 Brunswick大学跨学科研究学院）
Autonomous vehicles are being deployed with a spectrum of capability, extending from driver assistance features for the highway in personal vehicles (SAE Level 2+) to fully autonomous fleet ride sharing services operating in complex city environments (SAE Level 4+). This spectrum of autonomy often operates in different physical environments with different degrees of assumed driver in-the-loop oversight and hence have very different system and subsystem requirements. At the heart of SAE Level 2 to 5 systems is localization and mapping, which ranges from road determination for feature geofencing…
Institute of Power Machinery and Vehicular Engineering, College of Energy Engineering, Zhejiang University（浙江大学能源工程学院动力机械及车辆工程研究所）
An automated driving system should have the ability to supervise its own performance and to request human driver to take over when necessary. In the lane keeping scenario, the prediction of vehicle future trajectory is the key to realize safe and trustworthy driving automation. Previous studies on vehicle trajectory prediction mainly fall into two categories, i.e. physics-based and manoeuvre-based methods. Using a physics-based methodology, this paper proposes a lane departure prediction algorithm based on closed-loop vehicle dynamics model. We use extended Kalman filter to estimate the curren…
Michigan State University（密歇根州立大学）；Clemson University（克莱姆森大学）
It is expected that many human drivers will still prefer to drive themselves even if the self-driving technologies are ready. Therefore, human-driven vehicles and autonomous vehicles (AVs) will coexist in a mixed traffic for a long time. To enable AVs to safely and efficiently maneuver in this mixed traffic, it is critical that the AVs can understand how humans cope with risks and make driving-related decisions. On the other hand, the driving environment is highly dynamic and ever-changing, and it is thus difficult to enumerate all the scenarios and hard-code the controllers. To face up these …
Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM) networks, this work learns the relation between a driver's intention and the vehicle's changing positions relative to road infrastructures, and uses it to guide the prediction. Different from other state-of-the-art solutions, our work tre…
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 …
Unmanned vehicle technologies are an area of great interest in theory and practice today. These technologies have advanced considerably after the first applications have been implemented and cause a rapid change in human life. Autonomous vehicles are also a big part of these technologies. The most important action of a driver has to do is to follow the lanes on the way to the destination. By using image processing and artificial intelligence techniques, an autonomous vehicle can move successfully without a driver help. They can go from the initial point to the specified target by applying pre-…
Stanford University（斯坦福大学）；University of Maryland（马里兰大学）
We present a novel approach for risk-aware planning with human agents in multi-agent traffic scenarios. Our approach takes into account the wide range of human driver behaviors on the road, from aggressive maneuvers like speeding and overtaking, to conservative traits like driving slowly and conforming to the right-most lane. In our approach, we learn a mapping from a data-driven human driver behavior model called the CMetric to a driver's entropic risk preference. We then use the derived risk preference within a game-theoretic risk-sensitive planner to model risk-aware interactions among huma…
The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method that compares the performance of drivers or autonomous driving agents even if the environmental conditions are different, by using the driver behavioral advantage instead of absolute metrics, which efficiently removes the environmental factors. A concrete application of the metho…
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）
Missouri University of Science and Technology（密苏里科技大学）
Mobile and slow moving operations, such as striping, sweeping, bridge flushing and pothole patching, are critical for efficient and safe operation of the highway transportation system. A successfully implemented leader follower autonomous truck mounted attenuators system will eliminate all injuries to DOT employees in follow truck provided appropriate Statutory authority. The leader follower system design imposes more requirements to the lead truck drivers in order to ensure a safe and smooth system operation. The driver is now required to make driving decisions not only from the lead truck's …
An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sourc…
A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical …
Georgia Institute of Technology（佐治亚理工学院）；Monash University（莫纳什大学）；Honda Research Institute（本田研究院）
Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a planning framework where the degree to which the AV considers the other agent's reward is controlled by a selfishness factor. We test our approach on a simulated two-lane highway where the AV and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had l…
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…
Cyclops, introduced in this paper, is an open research platform for everyone that wants to validate novel ideas and approaches in the area of self-driving heavy-duty vehicle platooning. The platform consists of multiple 1/14 scale semi-trailer trucks, a scale proving ground, and associated computing, communication and control modules that enable self-driving on the proving ground. A perception system for each vehicle is composed of a lidar-based object tracking system and a lane detection/control system. The former is to maintain the gap to the leading vehicle and the latter is to maintain the…
Institute of Industrial Science, the University of Tokyo（东京大学工业科学研究所）
Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can su…
Corpy & Co., Inc.（Corpy公司）；The University of Tokyo（东京大学）
Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the physiological status of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane changes. The experimental conditions compri…
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）；Technical University of Darmstadt（达姆施塔特工业大学）
Shared steering control has been developed to reduce driver workload while keeping the driver in the control loop. A driver could integrate visual sensory information from the road ahead and haptic sensory information from the steering wheel to achieve better driving performance. Previous studies suggest that, compared with adaptive automation authority, fixed automation authority is not always appropriate with respect to human factors. This paper focuses on designing an adaptive shared steering control system via sEMG (surface electromyography) measurement from the forearm of the driver, and …
Haptic shared control is expected to achieve a smooth collaboration between humans and automated systems, because haptics facilitate mutual communication. A methodology for sharing a given task is important to achieve effective shared control. Therefore, the appropriate cooperative relationship between a human operator and automated system should be considered. This paper proposes a methodology to evaluate the cooperative status between the operator and the automated system in the haptic shared control of a steering operation using a pseudo-power pair of torque from each agent and the vehicle …
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…
Carnegie Mellon University（卡内基梅隆大学）；Honda Research Institute（本田研究所）；University of California, Berkeley（加利福尼亚大学伯克利分校）
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on heavy traffic where vehicles cannot change lanes without cooperating with other drivers. In this case, classical robust controls may not apply since there is no safe area to merge to without interacting with the other drivers. That said, modeling complex and interactive human behaviors is highly non-trivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive…
Chalmers University of Technology（查尔姆斯理工大学）；Volvo Group Trucks Technology（沃尔沃集团卡车技术公司）
This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore,…
Roundabouts in conjunction with other traffic scenarios, e.g., intersections, merging roadways, speed reduction zones, can induce congestion in a transportation network due to driver responses to various disturbances. Research efforts have shown that smoothing traffic flow and eliminating stop-and-go driving can both improve fuel efficiency of the vehicles and the throughput of a roundabout. In this paper, we validate an optimal control framework developed earlier in a multi-lane roundabout scenario using the University of Delaware's scaled smart city (UDSSC). We first provide conditions where…
As the advanced driver assistance system (ADAS) functions become more sophisticated, the strategies that properly coordinate interaction and communication among the ADAS functions are required for autonomous driving. This paper proposes a derivative-free optimization based imitation learning method for the decision maker that coordinates the proper ADAS functions. The proposed method is able to make decisions in multi-lane highways timely with the LIDAR data. The simulation-based evaluation verifies that the proposed method presents desired performance.
Safety Perspective on Assisted Lane Changes: Insights from Open-Road, Live-Traffic Experiments
European Commission, Joint Research Centre（欧洲委员会联合研究中心）；Vanderbilt University（范德堡大学）；European Commission, Directorate General for Internal Market, Industry, Entrepreneurship and SMEs（欧洲委员会内部市场、工业、创业和中小企业总司）
Lane-change is a fundamental driving behavior and highly associated with various types of collisions, such as rear-end collisions, sideswipe collisions, and angle collisions and the increased risk of a traffic crash. This study investigates effectiveness of different features categories combination in lane-changing intention prediction. Studies related to lane-changing intention prediction have been selected followed by strict standards. Then the meta-analysis was employed to not only evaluate the effectiveness of different features categories combination in lane-changing intention but also ca…
This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effe…
For lateral vehicle dynamics, planning trajectories for lane-keeping and lane-change can be generalized as a path planning task to stabilize a vehicle onto a target lane, which is a fundamental element in nowadays autonomous driving systems. On the other hand, two-point steering for lane-change and lane-keeping has been investigated by researchers from psychology as a sensorimotor mechanism of human drivers. In the first part of this paper, using knowledge of geometric control, we will first design a path planner which satisfies five design objectives: generalization for different vehicle mode…
Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning module should be carefully tuned, which needs great effort and expert knowledge. In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed. The learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation. The employed motion planner follows a widely accepted methodology that first samples…
University of Bamberg（班贝格大学）；IBM Deutschland GmbH（IBM德国有限公司）；fortiss GmbH（福蒂斯有限公司）
Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in the field of lane change prediction for manoeuvre anticipation. However, the decision-making process of such models is complex and non-transparent, hence reducing the trustworthiness of the smart solution. This work presents an innovative approach and a technical implementation for explaining lane change predictions of layer normalized LSTMs using Layer-wise Relevance Propagation (LRP). The core implementation includes …
