Scene Induced Multi-Modal Trajectory Forecasting via Planning (original) (raw)
Related papers
Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
ArXiv, 2020
In this paper, we address the problem of forecasting agent trajectories in unknown environments, conditioned on their past motion and scene structure. Trajectory forecasting is a challenging problem due to the large variation in scene structure, and the multi-modal nature of the distribution of future trajectories. Unlike prior approaches that directly learn one-to-many mappings from observed context, to multiple future trajectories, we propose to condition trajectory forecasts on \textit{plans} sampled from a grid based policy learned using maximum entropy inverse reinforcement learning policy (MaxEnt IRL). We reformulate MaxEnt IRL to allow the policy to jointly infer plausible agent goals and paths to those goals on a coarse 2-D grid defined over an unknown scene. We propose an attention based trajectory generator that generates continuous valued future trajectories conditioned on state sequences sampled from the MaxEnt policy. Quantitative and qualitative evaluation on the publi...
TNT: Target-driveN Trajectory Prediction
2020
Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and intrinsically multimodal. Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states. This leads to our target-driven trajectory prediction (TNT) framework. TNT has three stages which are trained end-to-end. It first predicts an agent's potential target states TTT steps into the future, by encoding its interactions with the environment and the other agents. TNT then generates trajectory state sequences conditioned on targets. A final stage estimates trajectory likelihoods and a final compact set of trajectory predictions is selected. This is in contrast to previous work which models agent intents as latent variables, and relies on test-time sampling to generate diverse trajectories. We benchmark TNT on trajectory predic...
Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting
2021
Trajectory forecasting is a crucial step for autonomous vehicles and mobile robots in order to navigate and interact safely. In order to handle the spatial interactions between objects, graph-based approaches have been proposed. These methods, however, model motion on a frameto-frame basis and do not provide a strong temporal model. To overcome this limitation, we propose a compact model called Spatial-Temporal Consistency Network (STC-Net). In STC-Net, dilated temporal convolutions are introduced to model long-range dependencies along each trajectory for better temporal modeling while graph convolutions are employed to model the spatial interaction among different trajectories. Furthermore, we propose a feature-wise convolution to generate the predicted trajectories in one pass and refine the forecast trajectories together with the reconstructed observed trajectories. We demonstrate that STC-Net generates spatially and temporally consistent trajectories and outperforms other graph-...
Anomaly Detection in Multi-Agent Trajectories for Automated Driving
ArXiv, 2021
Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we propose the spatio-temporal graph auto-encoder for learning normal driving behaviours. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents. To perform anomaly detection, we first estimate a density function of the learned trajectory feature representation and then detect anomalies in low-density regions. Due to the lack of multi-agent trajectory datasets for anomaly detection in automated driving, we introduce our dataset using a driving simulator for normal and abnormal manoeuvres. Our evaluations show that our approach learns the relation between different agents and delivers promising results compared to the related works. The code, simulation and the dataset are publicly available1.
Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation
ArXiv, 2021
Motion behaviour is driven by several factors goals, presence and actions of neighbouring agents, social relations, physical and social norms, the environment with its variable characteristics, and further. Most factors are not directly observable and must be modelled from context. Trajectory prediction, is thus a hard problem, and has seen increasing attention from researchers in the recent years. Prediction of motion, in application, must be realistic, diverse and controllable. In spite of increasing focus on multimodal trajectory generation, most methods still lack means for explicitly controlling different modes of the data generation. Further, most endeavours invest heavily in designing special mechanisms to learn the interactions in latent space. We present Conditional Speed GAN (CSG), that allows controlled generation of diverse and socially acceptable trajectories, based on user controlled speed. During prediction, CSG forecasts future speed from latent space and conditions ...
2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019
Vehicle trajectory prediction at intersections is both essential and challenging for autonomous vehicle navigation. This problem is aggravated when the traffic is predominantly composed of smaller vehicles that frequently disobey lane behavior as is the case in many developing countries. Existing macro approaches consider the trajectory prediction problem for lane-based traffic that cannot account when there is a high disparity in vehicle size and driving behavior among different vehicle types. Hence, we propose a vehicle trajectory prediction approach that models the interaction among different types of vehicles with vastly different driving styles. These interactions are encapsulated in the form of a social context embedded in a Generative Adversarial Network (GAN) to predict the trajectory of each vehicle at either a signalized or non-signalized intersection. The GAN model produces the most acceptable future trajectory among many choices that conform to past driving behavior as w...