In the recent vehicle trajectory prediction literature, the most common baselines are briefly introduced without the necessary information to reproduce it. In this article we produce reproducible vehicle prediction results from simple models. For that purpose, the process is explicit, and the code is available. Those baseline models are a constant velocity model and a single-vehicle prediction model. They are applied on the NGSIM US-101 and I-80 datasets using only relative positions. Thus, the process can be reproduced with any database containing tracking of vehicle positions. The evaluation reports Root Mean Squared Error (RMSE), Final Displacement Error (FDE), Negative Log-Likelihood (NLL), and Miss Rate (MR). The NLL estimation needs a careful definition because several formulations that differ from the mathematical definition are used in other works. This article is meant to be used along with the published code to establish baselines for further work. An extension is proposed to replace the constant velocity assumption with a learned model using a recurrent neural network. This brings good improvements in accuracy and uncertainty estimation and opens possibilities for both complex and interpretable models.
University of North Carolina Charlotte（北卡罗来纳大学夏洛特分校）
Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can be valuable for various traffic safety and management applications. Despite the abundance of trajectory datasets, few offer a comprehensive and diverse range of driving scenes, capturing multiple viewpoints of various highway layouts, merging lanes, and configurations. This limits their ability to capture the nuanced interactions between drivers, vehicles, and the roadway infrastructure. We introduce the \emph{Carolinas Highway Dataset (CHD\footnote{\emph{CHD} available at: \url{https://github.com/TeCSAR-UNCC/Carolinas…
Trajectory prediction is a critical technique in the navigation of robots and autonomous vehicles. However, the complex traffic and dynamic uncertainties yield challenges in the effectiveness and robustness in modeling. We purpose a data-driven approach socially aware Kalman neural networks (SAKNN) where the interaction layer and the Kalman layer are embedded in the architecture, resulting in a class of architectures with huge potential to directly learn from high variance sensor input and robustly generate low variance outcomes. The evaluation of our approach on NGSIM dataset demonstrates tha…
Department of Engineering and Computer Science, Syracuse University（锡拉丘兹大学工程与计算机科学系）；Syracuse University（锡拉丘兹大学）
The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors of surrounding vehicles, historical information of self-trajectory, and relative positions of neighbors, etc. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between th…
Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction
University of Macau（澳门大学）；UESTC（电子科技大学）；New York University（纽约大学）；Chongqing University（重庆大学）；Tongji University（同济大学）；Southeast University（东南大学）；Beihang University（北京航空航天大学）
To drive safely in complex traffic environments, autonomous vehicles need to make an accurate prediction of the future trajectories of nearby heterogeneous traffic agents (i.e., vehicles, pedestrians, bicyclists, etc). Due to the interactive nature, human drivers are accustomed to infer what the future situations will become if they are going to execute different maneuvers. To fully exploit the impacts of interactions, this paper proposes a ego-planning guided multi-graph convolutional network (EPG-MGCN) to predict the trajectories of heterogeneous agents using both historical trajectory infor…
MINES ParisTech（巴黎高等矿业学院）；PSL Research University（巴黎文理研究大学）；École des Ponts ParisTech（巴黎高科桥路学校）
In order to drive safely and efficiently on public roads, autonomous vehicles will have to understand the intentions of surrounding vehicles, and adapt their own behavior accordingly. If experienced human drivers are generally good at inferring other vehicles' motion up to a few seconds in the future, most current Advanced Driving Assistance Systems (ADAS) are unable to perform such medium-term forecasts, and are usually limited to high-likelihood situations such as emergency braking. In this article, we present a first step towards consistent trajectory prediction by introducing a long short-…
Nanyang Technological University（南洋理工大学）；University of Oxford（牛津大学）
Integrating trajectory prediction to the decision-making and planning modules of modular autonomous driving systems is expected to improve the safety and efficiency of self-driving vehicles. However, a vehicle's future trajectory prediction is a challenging task since it is affected by the social interactive behaviors of neighboring vehicles, and the number of neighboring vehicles can vary in different situations. This work proposes a GNN-RNN based Encoder-Decoder network for interaction-aware trajectory prediction, where vehicles' dynamics features are extracted from their historical tracks u…
Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles the spatial interactions using a graph convolutional network (GCN), and captures the temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory dis…
LIDAR has become an important part of many autonomous vehicles with its advantages on distance measurement and obstacle detection. LIDAR produces point clouds which have important information about surrounding environment. In this paper, we collected trajectory data on a two lane urban road using a Velodyne VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the sensor, some of these trajectories have missing points or gaps. In this paper, we propose a novel method for recovery of missing vehicle trajectory data points using microscopic traffic flow models. While short …
Predicting agents' future trajectories plays a crucial role in modern AI systems, yet it is challenging due to intricate interactions exhibited in multi-agent systems, especially when it comes to collision avoidance. To address this challenge, we propose to learn congestion patterns as contextual cues explicitly and devise a novel "Sense--Learn--Reason--Predict" framework by exploiting advantages of three different doctrines of thought, which yields the following desirable benefits: (i) Representing congestion as contextual cues via latent factors subsumes the concept of social force commonly …
Qualcomm AI Research（高通人工智能研究院）；Stanford University（斯坦福大学）
Predicting the behavior of surrounding vehicles is a critical problem in automated driving. We present a novel game theoretic behavior prediction model that achieves state of the art prediction accuracy by explicitly reasoning about possible future interaction between agents. We evaluate our approach on the NGSIM vehicle trajectory data set and demonstrate lower root mean square error than state-of-the-art methods.
Autonomous vehicles should be able to predict the future states of its environment and respond appropriately. Specifically, predicting the behavior of surrounding human drivers is vital for such platforms to share the same road with humans. Behavior of each of the surrounding vehicles is governed by the motion of its neighbor vehicles. This paper focuses on predicting the behavior of the surrounding vehicles of an autonomous vehicle on highways. We are motivated by improving the prediction accuracy when a surrounding vehicle performs lane change and highway merging maneuvers. We propose a nove…
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the basic abilities of an autonomous vehicle. In congestion, a vehicle's future movement is the result of its interaction with surrounding vehicles. A vehicle in congestion may have many neighbors in a relatively short distance, while only a small part of neighbors affect its future trajectory mostly. In this work, An interaction-aware method which predicts the future trajectory of an ego vehicle considering its interaction with eight surrounding vehicles is proposed. The dynamics of vehicles are encoded b…
Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people. To unlock this potential one of the critical aspects of the autonomous technology is understanding and predicting future movement of vehicles surrounding the SDV. This work presents a deep-learning-based method for kinematically feasible motion prediction of such traffic actors. Previous work did not explicitly encode vehicle kinematics and instead relied on the models to learn the constraints directly from the data, potentially resulting…
Beijing Institute of Technology（北京理工大学）；Delft University of Technology（代尔夫特理工大学）
Precisely modeling interactions and accurately predicting trajectories of surrounding vehicles are essential to the decision-making and path-planning of intelligent vehicles. This paper proposes a novel framework based on ensemble learning to improve the performance of trajectory predictions in interactive scenarios. The framework is termed Interactive Ensemble Trajectory Predictor (IETP). IETP assembles interaction-aware trajectory predictors as base learners to build an ensemble learner. Firstly, each base learner in IETP observes historical trajectories of vehicles in the scene. Then each b…
Highway driving places significant demands on human drivers and autonomous vehicles (AVs) alike due to high speeds and the complex interactions in dense traffic. Merging onto the highway poses additional challenges by limiting the amount of time available for decision-making. Predicting others' trajectories accurately and quickly is crucial to safely executing maneuvers. Many existing prediction methods based on neural networks have focused on modeling interactions to achieve better accuracy while assuming the existence of observation windows over 3s long. This paper proposes a novel probabili…
Model-based and learning-based methods are two major types of methodologies to model car following behaviors. Model-based methods describe the car-following behaviors with explicit mathematical equations, while learning-based methods focus on getting a mapping between inputs and outputs. Both types of methods have advantages and weaknesses. Meanwhile, most car-following models are generative and only consider the inputs of the speed, position, and acceleration of the last time step. To address these issues, this study proposes a novel framework called IDM-Follower that can generate a sequence …
University of Michigan（密歇根大学）；Ford Motor Company（福特汽车公司）
Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since there is no pre-defined number of interacting vehicles participate in, the prediction network has to be scalable with respect to the vehicle number in order to guarantee the consistency in terms of both accuracy and computational load. In this paper, the first fully scalable trajectory prediction network, SCALE-Net, is proposed that can ensure both higher prediction performance and consistent computational load regardles…
Prognostication of vehicle trajectories in unknown environments is intrinsically a challenging and difficult problem to solve. The behavior of such vehicles is highly influenced by surrounding traffic, road conditions, and rogue participants present in the environment. Moreover, the presence of pedestrians, traffic lights, stop signs, etc., makes it much harder to infer the behavior of various traffic agents. This paper attempts to solve the problem of Spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network. The Memory Neuron Net…
WeBank Co., Ltd.（微众银行）；Nanyang Technological University（南洋理工大学）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex scenes always yield great challenges in modeling the patterns of surrounding traffic. For example, one main challenge comes from the intractable interaction effects in a complex traffic system. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware acce…
Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments
University of Electronic Science and Technology of China（电子科技大学）；University of Macau（澳门大学）；Macau University of Science and Technology（澳门科技大学）；Tsinghua University（清华大学）
We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level information (road-agent behavior) from the extracted trajectory of each road-agent. Our formulation represents the proximity between the road agents using a weighted dynamic geometric graph (DGG). We use a two-stream graph-LSTM network to perform traffic forecasting using these weighted DGGs. The first stream predicts the spatial coordinates of road-agents, while…
Highway driving invariably combines high speeds with the need to interact closely with other drivers. Prediction methods enable autonomous vehicles (AVs) to anticipate drivers' future trajectories and plan accordingly. Kinematic methods for prediction have traditionally ignored the presence of other drivers, or made predictions only for a limited set of scenarios. Data-driven approaches fill this gap by learning from large datasets to predict trajectories in general scenarios. While they achieve high accuracy, they also lose the interpretability and tools for model validation enjoyed by kinema…
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
University of Macau（澳门大学）；State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学智慧城市物联网国家重点实验室）；Department of Civil and Environmental Engineering, University of Macau（澳门大学土木与环境工程系）
Institut de Recherche en Communications et Cybernétique de Nantes（南特通信与控制论研究所）
The subject of this paper is about the kinematic analysis and the trajectory planning of the Orthoglide 5-axis. The Orthoglide 5-axis a five degrees of freedom parallel kinematic machine developed at IRCCyN and is made up of a hybrid architecture, namely, a three degrees of freedom translational parallel manip-ulator mounted in series with a two degrees of freedom parallel spherical wrist. The simpler the kinematic modeling of the Or-thoglide 5-axis, the higher the maximum frequency of its control loop. Indeed, the control loop of a parallel kinematic machine should be computed with a high fre…
Many methods have been developed in trajectory planning in order to achieve smooth and accurate motion with considering the constraints of kinematics constraints such as angular position, velocity, acceleration, and jerk. The problem of using the combination of n-order polynomials is that there is no ideally match between the segments of trajectory path at the via point in terms of the number of kinematic constraints. It leads to generate undesirable trajectory path at the via point that connects between two segments of the trajectory path. In this paper, we aim to investigate the effect of in…
Motion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a novel motion prediction system for autonomous driving. Our system is based on the Bayesian inverse planning framework, which efficiently orchestrates map-based goal extraction, a classical control-based trajectory generator and a mixture of experts collection of light-weight neural networks specialised in motion profile prediction. In contrast to many alternative methods, this modularity helps isolate performance fac…
University of Utah Robotics Center（犹他大学机器人中心）；School of Computing, University of Utah（犹他大学计算机学院）
This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object. We implement our approach on an Allegro robot hand and perform thorough experiments on 10 objects from the YCB dataset. However, the proposed method is general enough to generate motions for most objects the robot can grasp. Experimental result s…
Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach
State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和澳门大学计算机与信息科学系）；State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和澳门大学土木与环境工程系、计算机与信息科学系）；Department of Civil and Environmental Engineering, University of Hawaii at Manoa（夏威夷大学马诺阿分校土木与环境工程系）；School of Vehicle and Mobility, Tsinghua University, Beijing, China（清华大学车辆与移动技术学院）
Eindhoven University of Technology（埃因霍温理工大学）；University of Twente（特文特大学）
Soft robots are interesting examples of hyper-redundancy in robotics, however, the nonlinear continuous dynamics of these robots and the use of hyper-elastic and visco-elastic materials makes modeling of these robots more complicated. This study presents a geometric Inverse Kinematic (IK) model for trajectory tracking of multi-segment extensible soft robots, where, each segment of the soft actuator is geometrically approximated with multiple rigid links connected with rotary and prismatic joints. Using optimization methods, the desired configuration variables of the soft actuator for the desir…
With the objective of improving the registration of LiDAR point clouds produced by kinematic scanning systems, we propose a novel trajectory adjustment procedure that leverages on the automated extraction of selected reliable 3D point--to--point correspondences between overlapping point clouds and their joint integration (adjustment) together with all raw inertial and GNSS observations. This is performed in a tightly coupled fashion using a Dynamic Network approach that results in an optimally compensated trajectory through modeling of errors at the sensor, rather than the trajectory, level. T…
An intelligent control strategy is proposed to eliminate the actuator saturation problem that exists in the trajectory tracking process of unmanned underwater vehicles (UUV). The control strategy consists of two parts: for the kinematic modeling part, a fuzzy logic-refined backstepping control is developed to achieve control velocities within acceptable ranges and errors of small fluctuations; on the basis of the velocities deducted by the improved kinematic control, the sliding mode control (SMC) is introduced in the dynamic modeling to obtain corresponding torques and forces that should be a…
This work addresses the problem of kinematic trajectory planning for mobile manipulators with non-holonomic constraints, and holonomic operational-space tracking constraints. We obtain whole-body trajectories and time-varying kinematic feedback controllers by solving a Constrained Sequential Linear Quadratic Optimal Control problem. The employed algorithm features high efficiency through a continuous-time formulation that benefits from adaptive step-size integrators and through linear complexity in the number of integration steps. In a first application example, we solve kinematic trajectory p…
