轨迹预测方法的发展需要度量指标来验证和比较其性能。目前已有的度量指标基于欧氏距离，这意味着误差在所有方向上被同等加权。欧氏度量对于道路这类结构化环境是不充分的，因为它们不能恰当地捕捉智能体相对于底层车道的意图。为了针对下游规划任务对轨迹预测方法提供合理的评估，我们提出了一种基于车道距离的新度量：车道漏检率（Lane Miss Rate，LMR）。在计算LMR时，真值和预测终点被分配到车道段，更准确地说是分配到它们的中心线。以沿车道段的距离来度量，与真值的距离在一定阈值范围内的预测计为命中，否则计为漏检。LMR被定义为产生漏检的序列所占的比例。我们在三个最先进的轨迹预测模型上的结果表明，LMR保持了基于欧氏距离度量的排序。与欧氏漏检率相比，定性结果表明，对于预测位于错误车道上的序列，LMR会产生漏检；另一方面，对于预测位于正确车道上的序列，则会产生命中。这意味着LMR隐式地相对于车道对欧氏误差进行加权，并朝着捕捉交通智能体意图的方向发展。LMR在Argoverse 2上的源代码已公开可用。
英文摘要：
The development of approaches for trajectory prediction requires metrics to validate and compare their performance. Currently established metrics are based on Euclidean distance, which means that errors are weighted equally in all directions. Euclidean metrics are insufficient for structured environments like roads, since they do not properly capture the agent's intent relative to the underlying lane. In order to provide a reasonable assessment of trajectory prediction approaches with regard to the downstream planning task, we propose a new metric that is lane distance-based: Lane Miss Rate (LMR). For the calculation of LMR, the ground-truth and predicted endpoints are assigned to lane segments, more precisely their centerlines. Measured by the distance along the lane segments, predictions that are within a certain threshold distance to the ground-truth count as hits, otherwise they count as misses. LMR is then defined as the ratio of sequences that yield a miss. Our results on three state-of-the-art trajectory prediction models show that LMR preserves the order of Euclidean distance-based metrics. In contrast to the Euclidean Miss Rate, qualitative results show that LMR yields misses for sequences where predictions are located on wrong lanes. Hits on the other hand result for sequences where predictions are located on the correct lane. This means that LMR implicitly weights Euclidean error relative to the lane and goes into the direction of capturing intents of traffic agents. The source code of LMR for Argoverse 2 is publicly available.
Beijing University of Posts and Telecommunications（北京邮电大学）
Predicting the plausible future trajectories of nearby agents is a core challenge for the safety of Autonomous Vehicles and it mainly depends on two external cues: the dynamic neighbor agents and static scene context. Recent approaches have made great progress in characterizing the two cues separately. However, they ignore the correlation between the two cues and most of them are difficult to achieve map-adaptive prediction. In this paper, we use lane as scene data and propose a staged network that Jointly learning Agent and Lane information for Multimodal Trajectory Prediction (JAL-MTP). JAL-…
Johns Hopkins University（约翰斯·霍普金斯大学）；Samsung Strategy and Innovation Center, Samsung, Inc.（三星战略与创新中心，三星公司）
Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs to know the current state of the surrounding objects but also their possible states in the future. As for vehicles, their trajectories are significantly influenced by the lane geometry and how to effectively use the lane information is of active interest. Most of the existing works use rasterized maps to explore road information, which does not distinguish different lanes. In this paper, we propose a novel instance-aware representation for lane representation. By integrating the lane features and traje…
Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior. This uncertainty can be loosely decoupled into lateral (e.g., keeping lane, turning) and longitudinal (e.g., accelerating, braking). We present a novel method that combines learned discrete policy rollouts with a focused decoder on subsets of the lane graph. The policy rollouts explore different goals given current observations, ensuring that the model captures lateral variability. Longitudinal variability is captured by our latent variable model decoder that i…
Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes. Bird's-eye-view roadmap information provides valuable information for making trajectory predictions, and while state-of-the-art models extract this information via image convolution, auxiliary loss functions can augment patterns inferred from deep learning by further encoding common knowledge of social and legal driving behaviors. Since human driving behavior is inherently multimodal, models which allow for multimodal output tend to outperform single-prediction mod…
LANet: A Lane Boundaries-Aware Approach For Robust Trajectory Prediction
Artificial Intelligence Creative Research Lab, ETRI University of Science（ETRI人工智能创意研究实验室，科学技术大学）；Artificial Intelligence Creative Research Lab, ETRI Daejeon, South Korea（ETRI人工智能创意研究实验室，韩国大田）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；MORAI Inc.（MORAI公司）；Hanyang University（汉阳大学）
Prior arts in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory. Such problem formulations and approaches, however, frequently lead to loss of diversity and biased trajectory predictions. Therefore, they are unsuitable for real-world autonomous driving where diverse and road-dependent multimodal trajectory predictions are critical for safety. To this end, this study proposes a novel loss function, \textit{Lane Loss}, that ensures map-adaptive diversity and accommodates geometric constraints. A two-stage tra…
Leibniz University Hannover（汉诺威莱布尼茨大学）；PhiGent Robotics（鉴智机器人）；University of Twente（特文特大学）；VISCODA GmbH（VISCODA有限公司）
Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints. Existing methods typically rely on one-stage trajectory prediction models, which condition future trajectories on observed trajectories combined with fused scene information. However, they often struggle with complex scene constraints, such as those encountered at intersections. To this end, we present a novel method, called LAformer. It uses a temporally dense lane-aware estimation module to select only the top highly potential lane segments in an HD…
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）
Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method
School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院广东省全驱系统控制理论与技术重点实验室）；Leapmotor Technology（零跑科技）
NEC Labs America（NEC美国实验室）；UC San Diego（加州大学圣迭戈分校）
Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Div…
Perception of the lane boundaries is crucial for the tasks related to autonomous trajectory control. In this paper, several methodologies for lane detection are discussed with an experimental illustration: Hough transformation, Blob analysis, and Bird's eye view. Following the abstraction of lane marks from the boundary, the next approach is applying a control law based on the perception to control steering and speed control. In the following, a comparative analysis is made between an open-loop response, PID control, and a neural network control law through graphical statistics. To get the per…
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…
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
In a previous study, we presented VT-Lane, a three-step framework for real-time vehicle detection, tracking, and turn movement classification at urban intersections. In this study, we present a case study incorporating the highly accurate trajectories and movement classification obtained via VT-Lane for the purpose of speed estimation and driver behavior calibration for traffic at urban intersections. First, we use a highly instrumented vehicle to verify the estimated speeds obtained from video inference. The results of the speed validation show that our method can estimate the average travel …
In the project, the vehicle was first controlled with ROS. For this purpose, the necessary nodes were prepared to be controlled with a joystick. Afterwards, DWA(Dynamic Window Approach), TEB(Timed-Elastic Band) and APF(Artificial Potential Field) path planning algorithms were applied to MIT RACECAR, respectively. These algorithms have advantages and disadvantages against each other on different issues. For this reason, a scenario was created to compare algorithms. On a curved double lane road created according to this scenario, MIT RACECAR has to follow the lanes and when it encounters an obst…
National Central University（国立中央大学）；Tokyo Metropolitan University（东京都立大学）；Auburn University（奥本大学）
Reasoning about vehicle path prediction is an essential and challenging problem for the safe operation of autonomous driving systems. There exist many research works for path prediction. However, most of them do not use lane information and are not based on the Transformer architecture. By utilizing different types of data collected from sensors equipped on the self-driving vehicles, we propose a path prediction system named Multi-modal Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents. To achieve more accurate path prediction, the Transformer…
Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction models is limited to metrics averaged over all samples in the dataset. We propose an automated methodology that allows to extract maneuvers (e.g., left turn, lane change) from agent trajectories in such datasets. The methodology considers information about the agent dynamics and information about the lane segments the agent traveled along. Although it is possible to use the resulting maneuvers for training classific…
Variational autoencoder (VAE) has widely been utilized for modeling data distributions because it is theoretically elegant, easy to train, and has nice manifold representations. However, when applied to image reconstruction and synthesis tasks, VAE shows the limitation that the generated sample tends to be blurry. We observe that a similar problem, in which the generated trajectory is located between adjacent lanes, often arises in VAE-based trajectory forecasting models. To mitigate this problem, we introduce a hierarchical latent structure into the VAE-based forecasting model. Based on the a…
Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions
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）；Bosch Center for Artificial Intelligence（博世人工智能中心）
Hanyang University（汉阳大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Hyundai Motor Company（现代汽车公司）
In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its environment. It is paramount to develop a prediction model that can exploit the contextual information in both static and dynamic environments surrounding the target agent and generate diverse trajectory samples that are meaningful in a traffic context. We propose a novel prediction model, referred to as the lane-aware prediction (LaPred) network, which uses the instance-level lane entities extracted from a semantic…
Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models
Beihang University（北京航空航天大学）；Research Institute for Frontier Science, Beihang University（北京航空航天大学前沿科学研究院）；Beijing University of Technology（北京工业大学）；College of Metropolitan Transportation, Beijing University of Technology（北京工业大学城市交通学院）
We address the curve lane detection problem which poses more realistic challenges than conventional lane detection for better facilitating modern assisted/autonomous driving systems. Current hand-designed lane detection methods are not robust enough to capture the curve lanes especially the remote parts due to the lack of modeling both long-range contextual information and detailed curve trajectory. In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information whil…
In this paper, we propose a novel approach for agent motion prediction in cluttered environments. One of the main challenges in predicting agent motion is accounting for location and context-specific information. Our main contribution is the concept of learning context maps to improve the prediction task. Context maps are a set of location-specific latent maps that are trained alongside the predictor. Thus, the proposed maps are capable of capturing location context beyond visual context cues (e.g. usual average speeds and typical trajectories) or predefined map primitives (such as lanes and s…
State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. However, building and maintaining HD maps is time-consuming and expensive. Furthermore, the HD maps assume structured environment such as the existence of major road and lanes, which are not present in rural areas. In this work, we propose an end-to-end transformer networks based approach for map-less autonomous driving. The proposed model takes raw LiDAR data and noisy topometric map as input and produces precise local trajectory for navigation. We demonstrate the effectiveness of ou…
We present a new method for multi-modal, long-term vehicle trajectory prediction. Our approach relies on using lane centerlines captured in rich maps of the environment to generate a set of proposed goal paths for each vehicle. Using these paths -- which are generated at run time and therefore dynamically adapt to the scene -- as spatial anchors, we predict a set of goal-based trajectories along with a categorical distribution over the goals. This approach allows us to directly model the goal-directed behavior of traffic actors, which unlocks the potential for more accurate long-term predictio…
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…
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China（香港理工大学电子与计算机工程系）；Voyager Research, Didi Chuxing, China（维杰研究，滴滴出行，中国）；Zhuoyu Technology Co., Ltd., Shenzhen, China（珠宇科技有限公司，深圳，中国）
