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DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker
TL;DR
Future prediction for interacting agents is uncertain because the same past scene can support multiple plausible outcomes, while existing methods may omit interactions, scene context, or long-term rewards. DESIRE combines stochastic hypothesis generation with context-aware reasoning and reward-based ranking and refinement, and its evaluations on KITTI and Stanford Drone report improvement over baselines.
Problem
Future prediction is ambiguous and existing methods may be deterministic or fail to fully account for interactions, semantic context, or long-term future rewards.
Method
DESIRE uses a CVAE to generate diverse hypotheses, then an RNN-based IOC scoring-regression framework to rank and refine them using scene context, interactions, and accumulated future rewards.
Results
The paper reports clear improvement over other baselines across driving and surveillance scenarios, including KITTI and Stanford Drone evaluations.
Takeaways & Limitations
DESIRE provides stochastic, continuous, time-profiled long-term predictions for multiple agents in complex scenes.
Takeaways & Limitations
The authors identify larger datasets as a direction for further improvement, and interaction modeling is handled separately from the initial per-agent sampling step.
Abstract
from arXiv · showhide
We introduce a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents. DESIRE achieves these in a single end-to-end trainable neural network model, while being computationally efficient. The model first obtains a diverse set of hypothetical future prediction samples employing a conditional variational autoencoder, which are ranked and refined by the following RNN scoring-regression module. Samples are scored by accounting for accumulated future rewards, which enables better long-term strategic decisions similar to IOC frameworks. An RNN scene context fusion module jointly captures past motion histories, the semantic scene context and interactions among multiple agents. A feedback mechanism iterates over the ranking and refinement to further boost the prediction accuracy. We evaluate our model on two publicly available datasets: KITTI and Stanford Drone Dataset. Our experiments show that the proposed model significantly improves the prediction accuracy compared to other baseline methods.
1. Introduction
DESIRE addresses uncertain future prediction in dynamic scenes by combining diverse hypotheses, scene and interaction reasoning, and long-term reward-based ranking and refinement. It is presented as an end-to-end deep-learning framework evaluated on driving and aerial-surveillance prediction tasks.
- Existing prediction methods may be deterministic or incompletely model agent interactions, semantic scene context, and long-term future rewards.
- DESIRE generates diverse future hypotheses to represent multiple plausible outcomes from the same observed situation.Its stochastic CVAE output is combined with RNN encodings of past observations.
- The framework reasons jointly over past motion, static and dynamic scene context, and interactions among multiple agents.
- The framework is demonstrated on traffic-participant prediction in KITTI and pedestrian prediction in the Stanford Drone dataset.The paper describes these applications as traffic-scene understanding for autonomous driving and behavior prediction in aerial surveillance.
- The paper presents scalability and end-to-end training as properties enabled by deep learning rather than hand-crafted features.
- DESIRE ranks and refines hypotheses using accumulated long-term future rewards, supporting strategic trajectory selection and improved distant predictions.
2. Related Works
Prior work includes classical trajectory models, interaction-aware predictors, recurrent sequence models, and deep generative approaches. DESIRE is positioned as addressing their remaining gaps in scene context, interactions, and continuous, time-profiled, long-term prediction.
- Classical approaches include Kalman filters, linear and Gaussian-process regression, autoregressive models, and time-series analysis.These methods can suffice when interactions with the scene or other agents are limited.
- Interaction-aware models account for other agents' behavior, including through shared hidden states for jointly reasoning across pedestrians.
- RNN encoder-decoder architectures are used because recurrent models naturally support sequence-to-sequence future prediction.
- Deep generative approaches use variational autoencoders to generate candidate predictions conditioned on past motions and latent variables.
- The related approaches are described as lacking one or more of scene context, interaction modeling, or continuous, time-profiled, long-term accurate predictions.
3. Method
DESIRE formulates multi-agent future prediction as posterior estimation from past trajectories and sensory input, combining stochastic sample generation with reward-based ranking, trajectory refinement, and scene-context fusion.
- Problem formulation: DESIRE estimates the posterior distribution of multiple agents’ future trajectories from past trajectories and sensory input.The method first samples diverse future predictions because direct optimization over continuous, high-dimensional trajectories is infeasible.
- Diverse Sample Generation with CVAE: A CVAE-based RNN encoder-decoder generates multiple future hypotheses by sampling latent variables conditioned on past trajectories.At test time, latent samples from the prior are decoded into prediction hypotheses; training uses reconstruction and KL-divergence losses.
- IOC-based Ranking and Refinement: The ranking module assigns sequential rewards to hypotheses and selects the final sample using accumulated future reward.This IOC-inspired scoring process evaluates prediction quality over the future horizon rather than only at individual time steps.
- IOC-based Ranking and Refinement: A learned displacement vector refines each prediction hypothesis using scene context and the dynamics of other agents.The regression function estimates an M × T displacement vector over the full prediction horizon, with M equal to the location-state dimension.
- Scene Context Fusion: Iterative feedback repeatedly updates regressed hypotheses, while the ranking and refinement module combines past motion, semantic scene context, and multi-agent interactions.The feedback process adjusts predictions after each refinement cycle; scene context is extracted with a CNN and interactions through fusion features.
4. Experiments
Experiments evaluate DESIRE on KITTI and SDD using multimodal prediction errors, oracle metrics, qualitative comparisons, and ablations. DESIRE benefits from multiple samples and iterative refinement, with performance varying by dataset interactions and prediction setting.
- Datasets: The evaluation uses KITTI driving scenes and Stanford Drone Dataset aerial videos, with approximately 16,000 SDD examples.KITTI examples combine images, Velodyne scans, calibration, semantic segmentation, and world-coordinate registration; SDD uses raw images and includes interacting high-density crowds.
- Evaluation Metrics and Baselines: Performance is measured with L2 distance, miss-rate, maximum errors, and top-K oracle error to account for future uncertainty.The main experiments set K to 50.
- Evaluation Metrics and Baselines: DESIRE is compared with Linear, RNN ED, RNN ED-SI, CVAE, and variants using semantic context, interactions, and iterative feedback.DESIRE-S uses semantic scene context, DESIRE-SI adds interactions, and DESIRE-X-ITN varies iterative feedback count.
- Analysis: Iterative regression refines DESIRE-SI samples toward the ground-truth trajectory, and increasing K slightly decreases top-20 oracle errors while best-sample errors remain similar.The past-length ablation reports that two seconds provides enough motion-context cues relative to one second.
- Results: Using more than a few CVAE samples lets CVAE outperform RNN ED, while DESIRE reaches baseline-level error with the top two DESIRE-SI-IT4 samples.On KITTI, RNN ED-SI can underperform RNN ED, whereas richer interactions produce clearer gains on SDD.
- Results: DESIRE-IT4 achieves the lowest top 10% prediction error in Table 1 across KITTI and SDD.The table caption identifies this result as addressing the multimodal nature of future prediction.
5. Conclusion
DESIRE is a stochastic deep-learning framework for distant future prediction of multiple agents in complex scenes. It combines static and dynamic context and improves over baseline methods in driving and surveillance evaluations.
- DESIRE predicts multiple agents’ distant futures with stochastic, continuous, time-profiled trajectories that account for future uncertainty.
- The framework incorporates both static and dynamic scene contexts within a deep IOC framework.
- Empirical evaluations in driving and surveillance scenarios demonstrate clear improvement over other baselines.
- The authors identify larger datasets and robotics applications using perspective images as directions for future improvement.