Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the safety of systems with neural network based components, especially in dense and highly interactive traffic environments. In this work, we propose a safety-driven interactive planning framework for neural network-based lane changing. To prevent over conservative planning, we identify the driving behavior of surrounding vehicles and assess their aggressiveness, and then adapt the planned trajectory for the ego vehicle accordingly in an interactive manner. The ego vehicle can proceed to change lanes if a safe evasion trajectory exists even in the predicted worst case; otherwise, it can stay around the current lateral position or return back to the original lane. We quantitatively demonstrate the effectiveness of our planner design and its advantage over baseline methods through extensive simulations with diverse and comprehensive experimental settings, as well as in real-world scenarios collected by an autonomous vehicle company.
Safe and Efficient Lane-Changing for Autonomous Vehicles: An Improved Double Quintic Polynomial Approach with Time-to-Collision Evaluation
Beijing University of Aeronautics and Astronautics（北京航空航天大学）；Changsha University of Science and Technology（长沙理工大学）；University Malaysia Perlis（马来西亚玻璃市大学）；University of Glasgow（格拉斯哥大学）；Taiyuan Normal University（太原师范学院）
Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior and quantifying uncertainty for highly interactive sce…
Efficient and Safe Planner for Automated Driving on Ramps Considering Unsatisfication
Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；School of Engineering, Cardiff University（卡迪夫大学工程学院）；School of Computing Science, University of Glasgow（格拉斯哥大学计算机科学学院）
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 …
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 …
Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（中国教育部系统控制与信息处理重点实验室）；School of Electronic Information Engineering, Shanghai Dianji University（上海电机学院电子信息工程学院）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；School of Electronics and Information Engineering, Soochow University（苏州大学电子信息工程学院）
Lane change for autonomous vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guarantee safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change method utilizing interactions between vehicles. We first propose an interactive trajectory prediction method to explore possible cooperations between an AV and the others. Further, an evaluation is designed to make a decision on lane change, in which s…
The ability to estimate human intentions and interact with human drivers intelligently is crucial for autonomous vehicles to successfully achieve their objectives. In this paper, we propose a game theoretic planning algorithm that models human opponents with an iterative reasoning framework and estimates human latent cognitive states through probabilistic inference and active learning. By modeling the interaction as a partially observable Markov decision process with adaptive state and action spaces, our algorithm is able to accomplish real-time lane changing tasks in a realistic driving simul…
Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
University of Southern California（南加州大学）；Virginia Tech（弗吉尼亚理工大学）；Miami University（迈阿密大学）；Northern Arizona University（北亚利桑那大学）；Northeastern University（东北大学）
Due to the complexity and volatility of the traffic environment, decision-making in autonomous driving is a significantly hard problem. In this project, we use a Deep Q-Network, along with rule-based constraints to make lane-changing decision. A safe and efficient lane change behavior may be obtained by combining high-level lateral decision-making with low-level rule-based trajectory monitoring. The agent is anticipated to perform appropriate lane-change maneuvers in a real-world-like udacity simulator after training it for a total of 100 episodes. The results shows that the rule-based DQN per…
ITU Artificial Intelligence and Data Science Research Center and Department of Aeronautical Engineering, Istanbul Technical University（伊斯坦布尔技术大学人工智能与数据科学研究中心和航空工程系）；Eatron Technologies；Faculty of Mechatronics Engineering, Istanbul Technical University（伊斯坦布尔技术大学机电一体化工程学院）；Department of Computer Engineering, University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校计算机工程系）；Faculty of Aeronautics and Astronautics, Aerospace Engineering, Istanbul Technical University（伊斯坦布尔技术大学航空航天学院航空航天工程系）；Istanbul Technical University（伊斯坦布尔技术大学）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Eatron Technologies, Istanbul, Turkey（土耳其伊斯坦布尔Eatron Technologies）；ITU Artificial Intelligence and Data Science Research Center（伊斯坦布尔技术大学人工智能与数据科学研究中心）；Department of Aeronautical Engineering, Istanbul Technical University（伊斯坦布尔技术大学航空工程系）
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.
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…
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…
California PATH, University of California, Berkeley（加州大学伯克利分校加利福尼亚先进交通技术合作伙伴计划）；GAC R&D Center Silicon Valley Inc.（广汽研发中心硅谷有限公司）
Recent advances in supervised learning and reinforcement learning have provided new opportunities to apply related methodologies to automated driving. However, there are still challenges to achieve automated driving maneuvers in dynamically changing environments. Supervised learning algorithms such as imitation learning can generalize to new environments by training on a large amount of labeled data, however, it can be often impractical or cost-prohibitive to obtain sufficient data for each new environment. Although reinforcement learning methods can mitigate this data-dependency issue by trai…
University of Pennsylvania（宾夕法尼亚大学）；Waymo（Waymo公司）
Interactive driving scenarios, such as lane changes, merges and unprotected turns, are some of the most challenging situations for autonomous driving. Planning in interactive scenarios requires accurately modeling the reactions of other agents to different future actions of the ego agent. We develop end-to-end models for conditional behavior prediction (CBP) that take as an input a query future trajectory for an ego-agent, and predict distributions over future trajectories for other agents conditioned on the query. Leveraging such a model, we develop a general-purpose agent interactivity score…
When the traffic stream is extremely congested and surrounding vehicles are not cooperative, the mandatory lane changing can be significantly difficult. In this work, we propose an interactive trajectory planner, which will firstly attempt to change lanes as long as safety is ensured. Based on receding horizon planning, the ego vehicle can abort or continue changing lanes according to surrounding vehicles' reactions. We demonstrate the performance of our planner in extensive simulations with eight surrounding vehicles, initial velocity ranging from 0.5 to 5 meters per second, and bumper to bum…
Automated lane changing is a critical feature for advanced autonomous driving systems. In recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded successful results in computing lane changing policies that strike a balance between safety, agility and compensating for traffic uncertainty. However, many RL algorithms exhibit simulator bias and policies trained on simple simulators do not generalize well to realistic traffic scenarios. In this work, we develop a data driven traffic simulator by training a generative adverserial network (GAN) on real life traject…
Characterizing Lane-Changing Behavior in Mixed Traffic
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign（土木与环境工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Civil and Environmental Engineering, George Washington University（土木与环境工程系，乔治·华盛顿大学）
Tongji University（同济大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Lane-changing (LC) behavior describes the lateral movement of the vehicle from the current-lane to the target-lane while proceeding forward. Among the many research directions, LC duration (LCD) measures the total time it takes for a vehicle to travel from the current lane to the target lane, which is an indispensable indicator to characterize the LC behavior. Although existing research has made some achievements, less attention has been paid to the research of heavy vehicles' LCD. Therefore, this paper aims to further explore the LCD between heavy vehicles and passenger cars. LC trajectories …
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…
Technical University of Munich（慕尼黑工业大学）；Carnegie Mellon University（卡内基梅隆大学）
Recently, autonomous driving has made substantial progress in addressing the most common traffic scenarios like intersection navigation and lane changing. However, most of these successes have been limited to scenarios with well-defined traffic rules and require minimal negotiation with other vehicles. In this paper, we introduce a previously unconsidered, yet everyday, high-conflict driving scenario requiring negotiations between agents of equal rights and priorities. There exists no centralized control structure and we do not allow communications. Therefore, it is unknown if other drivers ar…
Autonomy and connectivity are considered among the most promising technologies to improve safety, mobility, fuel and time consumption in transportation systems. Some of the fuel efficiency benefits of connected and automated vehicles (CAVs) can be realized through platooning. A platoon is a virtual train of CAVs that travel together following the platoon head, with small gaps between them. Vehicles may also reduce travel time by lane changing. In this paper, we devise an optimal control-based trajectory planning model that can provide safe and efficient trajectories for the subject vehicle and…
We introduce a prioritized system-optimal algorithm for mandatory lane change (MLC) behavior of connected and automated vehicles (CAV) from a dedicated lane. Our approach applies a cooperative lane change that prioritizes the decisions of lane changing vehicles which are closer to the end of the diverging zone (DZ), and optimizes the predicted total system travel time. Our experiments on synthetic data show that the proposed algorithm improves the traffic network efficiency by attaining higher speeds in the dedicated lane and earlier MLC positions while ensuring a low computational time. Our a…
Boston University（波士顿大学）；Honda Research Institute-US (HRI-US)（美国本田研究院（HRI-US））
We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the CAV "selfishly" only seeks to minimize its maneuver time, we seek to ensure that the fast-lane traffic flow is minimally disrupted (through a properly defined metric). Additionally, when performing lane-changing maneuvers, we optimally sel…
An ACO-MPC Framework for Energy-Efficient and Collision-Free Path Planning in Autonomous Maritime Navigation
School of Engineering, University of Strathclyde（斯特拉思克莱德大学工程学院）；James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）；School of Power and energy, Northwestern Polytechnical University（西北工业大学动力与能源学院）；School of Engineering, University of Birmingham（伯明翰大学工程学院）
Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning
College of Information and Engineering, Minzu University of China（中国民族大学信息与工程学院）；Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China（教育部民族语言智能分析与安全治理重点实验室）；Institute for Artificial Intelligence, Peking University（北京大学人工智能学院）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）
Automated lane change is one of the most challenging task to be solved of highly automated vehicles due to its safety-critical, uncertain and multi-agent nature. This paper presents the novel deployment of the state of art Q learning method, namely Rainbow DQN, that uses a new safety driven rewarding scheme to tackle the issues in an dynamic and uncertain simulation environment. We present various comparative results to show that our novel approach of having reward feedback from the safety layer dramatically increases both the agent's performance and sample efficiency. Furthermore, through the…
University of California, Berkeley（加州大学伯克利分校）；Xi’an Jiaotong University（西安交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；MINES ParisTech（巴黎高等矿业学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Behavior-related research areas such as motion prediction/planning, representation/imitation learning, behavior modeling/generation, and algorithm testing, require support from high-quality motion datasets containing interactive driving scenarios with different driving cultures. In this paper, we present an INTERnational, Adversarial and Cooperative moTION dataset (INTERACTION dataset) in interactive driving scenarios with semantic maps. Five features of the dataset are highlighted. 1) The interactive driving scenarios are diverse, including urban/highway/ramp merging and lane changes, roundab…
Tongji University（同济大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
In order to minimize the impact of lane change (LC) maneuver on surrounding traffic environment, a hierarchical automatic LC algorithm that could realize local optimum has been proposed. This algorithm consists of a tactical layer planner and an operational layer controller. The former generates a local-optimum trajectory. The comfort, efficiency, and safety of the LC vehicle and its surrounding vehicles are simultaneously satisfied in the optimization objective function. The later is designed based on vehicle kinematics model and the Model Predictive Control (MPC), which aims to minimize the …
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…
Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency
Department of Mechanical Engineering（机械工程系）；Department of Electrical Engineering（电气工程系）；National University of Singapore（新加坡国立大学）；Computer Science（计算机科学）；University of Michigan（密歇根大学）
Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we proposed a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treated both state space and action space as continuous, and de…
Honda Research Institute（本田研究所）；University of California, Berkeley（加州大学伯克利分校）
This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic flow. When lane-changing is discretionary, it is advised not to change lanes unless highly beneficial, e.g., reducing travel time significantly or securing higher safety. Evaluating such "benefit" is a challenge, along with multiple surrounding vehicles in dynamic speed and heading with uncertainty. We propose a real-time lane-selection algorithm with careful cost considerations and with modularity in design. The alg…
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…
This paper introduces Sentinel, an onboard system for intelligent vehicles that guides their lane changing behavior during a freeway incident with the goal of reducing traffic congestion, capacity drop, and delay. When an incident blocking the lanes ahead is detected, Sentinel calculates the probability of leaving the blocked lane(s) before reaching the incident point at each time step. It advises the vehicle to leave the blocked lane(s) when that probability drops below a certain threshold, as the vehicle nears the congestion boundary. By doing this, Sentinel reduces the number of late-stage …
Automatic driving lane change safety prediction model based on LSTM
Trine University（特莱恩大学）；Yantai University（烟台大学）；University of Electronic Science and Technology of China（电子科技大学）；Northern Arizona University（北亚利桑那大学）；University of Aberdeen（阿伯丁大学）
An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment
Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）智能交通研究所，系统中心）；Guangdong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things（广东省集成通信、感知与计算 ubiquitous internet of things 关键实验室）；Robotics and Autonomous Systems Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）机器人与自主系统研究所）
Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields
School of Computing Science, University of Glasgow（格拉斯哥大学计算机科学学院）；College of Mechanical Engineering, Chongqing University of Technology（重庆理工大学机械工程学院）；Laboratory of Intelligent Home Appliances, College of Science and Technology, Ningbo University（宁波大学智能家电实验室）
Graz University of Technology（格拉茨工业大学）；Institute for Software Technology（软件技术研究所）；Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems（克里斯蒂安·多普勒自主网络物理系统质量保障方法实验室）
In this paper, we outline an approach for automatic generation of challenging road networks for virtual testing of an automated lane keep system. Based on a set of control points, we construct a parametric curve that represents a road network, which defines the dynamic driving task an automated lane keep system equipped vehicle has to perform. Changing control points has global influence on the resulting road geometry. Our approach uses search to find a set of control points that results in a challenging road geometry, eventually forcing the vehicle to leave the intended path. We evaluated our…
