Forecasting Interactive Dynamics of Pedestrians with Fictitious Play

By Wei-Chiu Ma, De-An Huang, Namhoon Lee, Kris M. Kitani. 2016

166 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

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Citations in the corpus

Deep Imitative Models for Flexible Inference, Planning, and Control (2018, background): “Typically this involves recurrent neural networks combined with Gaussian density layers or generative models based on some context inputs such as LIDAR, images, or known positions of external agents (Lee et al., 2017; Schmerling et al., 2018; Zyner et al., 2018; Gupta et al., 2018; Ma et al., 2017).”
Human motion trajectory prediction: a survey (2019, methodology): “In the following we review several inverse planning approaches that predict multi-agent motions (Kuderer et al. 2012; Kretzschmar et al. 2014; Pfeiffer et al. 2016; Ma et al. 2017; Lee et al. 2017).”
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction (2019, methodology): “Our model contrasts with popular past approaches which either provide only a single MAP trajectory [1, 2, 9, 10, 11] or an unweighted set of samples via a generative model [3, 4, 6, 12, 13, 14, 15].”
TNT: Target-driveN Trajectory Prediction (2020, background): “Trajectory prediction has received much attention recently, especially in autonomous driving [9, 10, 12, 13, 14], in social interaction prediction [15, 16, 17, 18] and sports [19, 20].”
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings (2019, background): “Multi-agent forecasting: Data-driven approaches have been applied to forecast complex interactions between multiple pedestrians [1, 3, 10, 14, 21], vehicles [6, 19, 26], and athletes [9, 18, 20, 34, 36, 37].”
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net (2018, unknown): “[19] proposed to utilize concepts from game theory to model the interaction between pedestrian while predicting future trajectories.”
SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction (2019, background): “Some studies based on game theory model the interaction among pedestrian ﬂows [17, 50] and evacuation process [2, 16, 53], Ma et al. [27] predict pedestrians from a static frame using ﬁc-titious play.”
r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting (2018, background): “In [5,23,28,50] future pedestrian trajectories are predicted from surveillance video.”
Future Person Localization in First-Person Videos (2017, background): “Given both locations of start and destination, work based on inverse reinforcement learning can forecast in-between paths [17, 24].”
Ego-Pose Estimation and Forecasting As Real-Time PD Control (2019, background): “Plenty of work has investigated third-person [61, 31, 3, 21, 1, 43, 65] and ﬁrst-person [51] trajectory forecasting, but this line of work only forecasts a person’s future positions instead of poses.”
IntentNet: Learning to Predict Intention from Raw Sensor Data (2018, background): “[21] proposed to couple game theory and deep learning to model pedestrians interactions and estimate person-specific behavior parameters.”