Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors
Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn multimodal behaviors of other agents from data. In this paper, we present an algorithm called multi-predictor fusion (MPF) that augments the performance of learning-based predictors by imbuing them with motion planners that are tasked with satisfying logic-based rules. MPF probabilistically combines learning- and rule-based predictors by mixing trajectories from both standalone predictors in accordance with a belief distribution that reflects the online performance of each predictor. In our results, we show that MPF outperforms the two standalone predictors on various metrics and delivers the most consistent performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesTrajectory PredictionSimilar Papers 제목 키워드 기반
MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion
We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimo…
Motion Forecastingmotion predictionPredictionMultiple Future Prediction Leveraging Synthetic Trajectories
Trajectory prediction is an important task, especially in autonomous driving. The ability to forecast the position of other moving agents can yield to an effective planning, ensuring safety for the autonomous vehicle as …
Autonomous DrivingFuture predictionMulti Future Trajectory PredictionPosition+2Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction
Predicting pedestrian motion trajectories is critical for the path planning and motion control of autonomous vehicles. Recent diffusion-based models have shown promising results in capturing the inherent stochasticity of…
Trajectory PredictionAutonomous VehiclesRECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection
Autonomous driving stacks must pick one trajectory from a multi-modal candidate set; choosing by model confidence ignores safety, traffic-law, and comfort constraints. We present \textsc{RECTOR} (Rule-Enforced Constraine…
Autonomous DrivingBridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision
Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Althoug…
Autonomous DrivingPredictionTrajectory Prediction