Source-linked AI summary
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
Haoran Song, Wenchao Ding, Yuxuan Chen, Shaojie Shen, Michael Yu Wang, Qifeng Chen
TL;DR
Autonomous driving requires socially compliant prediction of surrounding vehicles despite interaction and behavioral uncertainty. PiP conditions multi-agent prediction on the ego vehicle’s candidate future trajectories, achieving state-of-the-art highway forecasting and coupling prediction with planning for interactive scenarios.
Problem
Existing prediction commonly relies on history and is decoupled from planning, although different ego plans can substantially affect surrounding vehicles’ future behavior.
Method
PiP combines surrounding-agent history with candidate future plans of the controllable ego vehicle and models their coupled future interactions.
Results
PiP outperforms state-of-the-art methods for multi-agent forecasting on highway datasets and produces predictions one-to-one corresponding to candidate ego trajectories.
Takeaways & Limitations
The planning-prediction-coupled pipeline provides a usable interface for planning in dense or fast-moving traffic.
Abstract
from arXiv · showhide
It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled with planning. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets. Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.
1 Introduction
PiP conditions multi-agent trajectory prediction on candidate future plans of the ego vehicle, coupling prediction with planning to model interaction and support flexible behavior in interactive traffic.
- Planning-informed prediction: The proposed model aims to improve predictive accuracy by capturing interaction and reducing uncertainty from multimodal driving behavior.Its contributions include planning information in social-context encoding and fully convolutional modeling of future target dependencies.
- Planning-informed prediction: PiP incorporates the ego vehicle’s candidate future plans alongside surrounding vehicles’ history tracks to predict their trajectories.The framework uses candidate “what-if” plans rather than requiring the ego’s exact future trajectory.
- Planning-prediction coupling: PiP produces predictions one-to-one corresponding to candidate ego trajectories, enabling planning to evaluate how surrounding vehicles may respond to each plan.This pipeline is intended for dense, highly interactive scenarios such as merging into a congested lane.
- Planning-informed prediction: The planning-coupled module models multi-agent interaction across both surrounding-agent history and the controlled agent’s future planning.A target fusion module further captures interdependency among predicted target agents.
2 Related Work
Prior work models interaction-aware trajectory prediction and typically integrates prediction with planning through fixed, history-based predictions. Planning-informed approaches begin conditioning prediction on ego information, but earlier work uses only goal position.
- Interaction-aware trajectory prediction: Interaction-aware trajectory prediction methods model interactions among agents and, in some cases, between agents and the scene.The literature includes deterministic, GAN-based, and VAE-based approaches, with extensions to heterogeneous traffic and autonomous driving.
- Trajectory prediction for control and planning: Common vehicle-planning workflows roll out multiple ego trajectories, score them using user-defined functions and fixed history-based predictions of other vehicles, then select one.The prediction result for other vehicles is fixed across different ego candidates.
- Planning-informed trajectory prediction: Earlier planning-informed work conditions prediction on the ego vehicle’s goal position, but goal information alone may restrict prediction in scenarios with different insertion behaviors.The paper motivates richer conditioning on candidate future trajectories rather than only a destination.
3 Method
PiP predicts multiple surrounding vehicles by conditioning interaction-aware representations on the ego vehicle’s planned trajectory, fusing target encodings, and decoding maneuver-conditioned multimodal futures.
- Problem formulation: PiP estimates multiple target vehicles’ future trajectories from surrounding-vehicle histories together with the controllable ego vehicle’s planned future.The objective is the posterior P(Y|X, I), where X contains past trajectories and I contains ego planning.
- Planning coupled module: The planning-coupled module encodes each target’s dynamics, neighboring-agent interactions, and spatial dependency on ego planning within a target-centric area.It processes the ego vehicle, target vehicle, and nearby vehicles using separate dynamic, social, and planning-related encoding streams.
- Planning coupled module: Observation and planning tensors are built from spatially placed trajectory encodings, processed in parallel, and merged into a social encoding covering past and future context.The ego plan is encoded in reverse order so nearer-future portions receive greater weight because they are considered more reliable.
- Target fusion module: Target encodings are placed in an ego-centric grid and fused to model interdependencies before each target’s fused representation is decoded.The fused tensor preserves spatial structure, allowing each target encoding to be selected by its grid location.
- Maneuver-based decoding: The maneuver-based decoder predicts six predefined lateral-longitudinal maneuver classes and their probabilities, then generates trajectory distributions for each target.Maneuvers combine lane-keeping or lane changes with normal driving or braking; outputs include future displacements and standard deviations.
- Training and implementation: Training minimizes the negative log likelihood of target future trajectories under their true maneuver classes.The implementation selects targets within a 60.96×10.67-meter area discretized as a 25×5 spatial grid.
4 Experiments
PiP is evaluated on NGSIM and HighD using quantitative forecasting metrics, ablations, and simulated scenarios with diverse ego-vehicle plans. Results show improved predictive accuracy and meaningful predictions across varied maneuvers, including collisions and safe lane changes.
- Experimental Setup: Evaluation uses NGSIM and HighD trajectories, with 3 seconds of history and a 5-second prediction horizon.The datasets are split into training, testing, and evaluation subsets; ego planning inputs are downsampled to 1 Hz during testing and evaluation.
- Experimental Setup: PiP is compared with deterministic and stochastic baselines, while PiP-noPlan and PiP-noFusion isolate the planning-coupled and target-fusion modules.The baselines include S-LSTM, CS-LSTM, S-GAN, and MATF.
- Quantitative Evaluation: PiP significantly outperforms S-LSTM and CS-LSTM in both RMSE and NLL on both datasets.Its RMSE is also lower than stochastic models when those models sample three trajectories, although their reported RMSE uses the minimum error among samples.
- Quantitative Evaluation: Consistent NLL and RMSE improvements indicate that incorporating ego-vehicle planning and correlations among targets improves predictive accuracy.The ablation results further show that PiP-noPlan performs worse than PiP-noFusion in most cases, highlighting the effectiveness of planning information.
- User Study: In a user study with 25 participants and 20 video pairs, PiP simulations were judged reasonable in 47.8% of selections.The simulated situation was selected as unreasonable in 52.2% of selections, partly because its ego trajectory was generated offline rather than through adaptive replanning.
- Qualitative Analysis: Qualitative scenarios show PiP adapting predictions to planned maneuvers, forecasting both collisions and feasible lane changes across NGSIM and HighD scenes.Examples include deceleration by a following vehicle during agile merging, collision forecasts under risky plans, and safe merging under a suitable plan.
5 Conclusion
PiP predicts surrounding vehicles using the ego vehicle’s future planning, outperforming state-of-the-art multi-agent forecasting on highway datasets and coupling prediction with planning.
- 5 Conclusion: PiP predicts surrounding agents by incorporating the controllable vehicle’s future planning alongside historical tracks.The framework conditions predictions on candidate ego trajectories rather than an exact future trajectory.
- 5 Conclusion: PiP outperforms state-of-the-art methods for multi-agent forecasting on highway datasets.
- 5 Conclusion: PiP produces future predictions corresponding one-to-one with candidate trajectories, providing an interface for planning in dense or fast-moving traffic.
- 5 Conclusion: The authors plan to extend PiP to imperfect tracking or detection and integrate prediction with trajectory generation in an interactive motion planner.