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Interpretable Social Anchors for Human Trajectory Forecasting in Crowds
Parth Kothari, Brian Sifringer, Alexandre Alahi
TL;DR
Crowd forecasting must account for social interactions, while data-driven methods lack interpretability and first-order hand-designed methods struggle with complex, long-term motion. This paper combines discrete choice models for interpretable intents with neural scene-specific residuals. On synthetic and real-world data, it performs at par with competitive baselines while providing rationale for high-level decisions.
Problem
Crowd forecasting must capture social interactions, but first-order hand-designed models struggle with complex, long-term motion.
Source: §1 · p. 1Method
A discrete choice model selects interpretable, speed- and direction-based intents, while neural networks add scene-specific residuals for continuous refinements and complex interactions.
Source: §1 · p. 2, §3.3 · p. 4Results
Takeaways & Limitations
The method provides rationale for high-level decisions, an identified need for safety-critical applications such as autonomous systems.
Source: §1 · p. 2Hand-crafted knowledge-based functions cannot capture trajectory heterogeneity in complex situations, including long-term dependencies and subtle social interactions; neural terms help alleviate this limitation.
Source: §3.4 · p. 5
Abstract
from arXiv · showhide
Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions. In recent years, neural network-based methods have been shown to outperform hand-crafted methods on distance-based metrics. However, these data-driven methods still suffer from one crucial limitation: lack of interpretability. To overcome this limitation, we leverage the power of discrete choice models to learn interpretable rule-based intents, and subsequently utilise the expressibility of neural networks to model scene-specific residual. Extensive experimentation on the interaction-centric benchmark TrajNet++ demonstrates the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.
1. Introduction
Crowd trajectory forecasting must account for social motion, but existing approaches trade accuracy against interpretability: hand-crafted models can explain predictions yet struggle with complex, long-term motion, while neural models lack decision-making explanations. The paper combines interpretable discrete intents with neural scene-specific refinements and reports competitive accuracy alongside explanatory predictions.
1. Introduction
- Hand-crafted models can represent collision avoidance and leader-following, but their first-order design struggles to predict accurate trajectories over complex, long-term motion.The paper frames the task as forecasting social motion, including avoiding collisions and yielding right-of-way.Source: §1 · p. 1
- Neural-network trajectory models outperform hand-crafted approaches on distance-based metrics, but their decision-making process lacks interpretability.Social LSTM is cited as the first neural-network-based trajectory forecasting model.Source: §1 · p. 1
- The proposed model predicts a probability distribution over future intents defined by each pedestrian’s speed and direction, using an interpretable choice-model architecture.The framework augments knowledge-based functions with neural-network representations without compromising interpretability.Source: §1 · p. 2
- A neural network adds scene-specific residuals to high-level intents, expanding discrete predictions to continuous outputs and capturing complex interactions and long-term dependencies.The architecture separates coarse intents from scene-specific nuances of human motion.Source: §1 · p. 2
- On TrajNet++, the method performs at par with competitive baselines on real-world and synthetic datasets while providing a rationale for high-level decisions.The authors identify this interpretability as important for safety-critical applications such as autonomous systems.Source: §1 · p. 2
2. Related Work
Prior work models crowd interactions with interpretable knowledge-based rules or data-driven neural networks, while multimodal forecasting methods seek distributions over possible futures. This paper combines rule-based intents with neural refinements and frames multimodality as a ranked distribution over intents.
2.1. Social Interactions
- Knowledge-based social-interaction models, including discrete choice models, offer interpretable outputs and design flexibility but can be too simple to capture complex human interactions.Discrete choice modelling has been applied to pedestrian flows, group walking, collision avoidance, and critical or emergency situations,,, [61].Source: §2.1 · p. 2
- Neural models capture social interactions and outperform knowledge-based works on distance-based metrics, but their predictions are not explainable like knowledge-based outputs.Approaches include social pooling and attention mechanisms, [21] [28] [38], [18] [35] [53] [31] [6] [17].Source: §2.1 · p. 2
- The paper combines interpretable rule-based models for high-level intents with neural-network residuals that account for scene-specific and long-term motion characteristics.The proposed approach addresses the contrasting strengths of knowledge-based and neural-network models.Source: §2.1 · p. 2
2.2. Multimodality
- One solution to multimodal forecasting is explicitly outputting multiple modes with a decoder, such as Mixture Density Networks, though this training technique suffers numerical instabilities, often leading to mode collapse.Generative models implicitly model future-trajectory distributions conditioned on the past scene, naturally offering the possibility of multiple samples [33] [28] [21] [6].Source: §2.2 · p. 2
- The paper recasts multimodality as a distribution over agent intents, using choice models to provide an interpretable rationale and ranking for each future mode.At each step, the model combines a discretized set of future intents with scene-specific refinements.Source: §2.2 · p. 3, Figure 2 · p. 3
3. Method
The method combines interpretable discrete-choice predictions of future intents with neural, scene-specific refinements to forecast pedestrians’ trajectories. The refinements address long-term dependencies and complex social interactions that hand-crafted rules alone do not capture.
3.1. Problem Definition
- The method forecasts all pedestrians’ future trajectories simultaneously from their observed trajectories, with each person’s past and future represented as time-indexed positions.Source: §3.1 · p. 3
3.3. Model Architecture
- The model predicts a distribution over discrete future intents, or social anchors, and adds a scene-specific residual to each to return predictions to a continuous output space.The anchor set is sized by the number of speed levels and direction changes, and is intended to cover the output space so residuals remain small.Source: §3.3 · p. 4
- Neural scene residuals model long-term motion dependencies and subtle social interactions that first-order hand-crafted rules cannot capture.The architecture encodes pedestrian velocity and social interactions, then uses recurrent hidden states to predict residuals for each anchor.Source: §3.3 · p. 4
3.4. Anchor Selection
- A DCM selects among normalized future intentions using interpretable motion rules for avoiding occupied space and collisions, keeping direction, and following leaders.The model uses the Learning Multinomial Logit framework with hand-designed and neural terms in its utility.Source: §3.4 · pp. 4–5, §3.2 · p. 4
- The DCM’s performance depends on its hand-crafted motion functions, which may miss heterogeneous decisions, long-term dependencies, and subtle interactions in complex situations.Neural terms are added to help address these limitations, and the framework can incorporate other knowledge-driven functions.Source: §3.4 · p. 5
- Training minimizes negative log-likelihood and penalizes the anchor closest to ground-truth velocity; at test time, predicted positions feed subsequent forecasting steps.The closest anchor is measured by L2 distance in state-sequence space.Source: §3.4 · p. 5
3.5. Implementation Details
- Implementation uses directional pooling with a 16 × 16 grid at 0.6-meter resolution and trains with ADAM at a 1e-3 learning rate for 25 epochs.Source: §3.5 · p. 5
4. Experiments
On TrajNet++, the proposed method provides interpretable predictions without compromising accuracy on distance-based metrics against competitive baselines. The experiments also show how behavioral maps inform intent selection and how direction normalization affects performance and learned representations.
4. Experiments
- TrajNet++ includes roughly 200,000 real-world scenes sampled for social interactions, with no collisions in the training or testing set.The scenes include group behavior, people crossing, collision avoidance, and groups forming or dispersing.Source: §4 · p. 5
- Evaluation uses ADE, FDE, Col-I, and top-3 ADE/FDE, comparing against S-LSTM, WTA, SGAN, CVAE, and MinK.SAnchor uses 15 anchors: five angle profiles and three speed profiles.Source: §4 · p. 6
- The method provides interpretable predictions without compromising distance-based accuracy against competitive baselines on TrajNet++ synthetic and real data.Tables 1 and 2 report ADE/FDE in meters and Col I in percent for predictions after 9 observed and 12 predicted time steps.Source: Table 1 · p. 6, Table 2 · p. 6, §4 · p. 6
4.1. Interpretability of the Intents
- In the illustrated turn-left case, collision-avoidance and occupancy maps strongly influence the selected intent while the pedestrian maintains speed.The figure also shows current neighbor positions in blue, velocities in green, and the ground-truth choice in light green.Source: §4.1 · p. 6, Figure 3 · p. 7
- In two leader-follower cases, a strong leader-follower map favors slowing down over the neural network’s constant-speed preference, while a weaker map leaves that preference dominant.The resulting intents are slowing down in the second case and maintaining constant speed and direction in the third.Source: §4.1 · p. 6, Figure 3 · p. 7
4.2. Direction Normalization
- Direction normalization rotates the scene so the pedestrian faces a common direction at each time step, enabling dynamic, speed- and direction-dependent DCM anchors.The authors state that this integration retains prediction accuracy and makes the model rotation-invariant at each time step.Source: §4.2 · p. 7
- Direction normalization improves every reported metric across models on synthetic data and improves prediction-collision performance on real data.The comparisons use baselines and their direction-normalized versions; (N) denotes the normalized version in Tables 3 and 4.Source: §4.2 · p. 8, Table 3 · p. 8, Table 4 · p. 8
- Direction-normalized S-LSTM representations have nearest neighbors more semantically similar in both focal-pedestrian trajectories and neighborhood configurations.Figure 4 shows the top four nearest training-set representations for a two-pedestrian interaction toy dataset.Source: §4.2 · p. 8, Figure 4 · p. 8
5. Conclusions
The approach separates human motion into high-level discrete intents and low-level scene-specific refinements, using interpretable knowledge-based functions alongside neural predictions. Experiments on synthetic and real data show interpretable predictions and accurate forecasts, with scene-specific refinements extending the discrete problem into the continuous domain.
5. Conclusions
- The model selects discretized intents using both interpretable knowledge-based functions and neural network predictions from the scene.The knowledge-based functions expose motion rules, while neural predictions handle long-term dependencies and complex human interactions.Source: §5 · p. 8
- Experiments on synthetic and real data demonstrate the method’s interpretability and accurate predictions.Source: §5 · p. 8
- Scene-specific refinements cast the discrete prediction problem into the continuous domain.Source: §5 · p. 8