Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions

Abstract

As the potential for autonomous vehicles to be integrated on a large scale into modern traffic systems continues to grow, ensuring safe navigation in dynamic environments is crucial for smooth integration. To guarantee safety and prevent collisions, autonomous vehicles must be capable of accurately predicting the trajectories of surrounding traffic agents. Over the past decade, significant efforts from both academia and industry have been dedicated to designing solutions for precise trajectory forecasting. These efforts have produced a diverse range of approaches, raising questions about the differences between these methods and whether trajectory prediction challenges have been fully addressed. This paper reviews a substantial portion of recent trajectory prediction methods proposing a taxonomy to classify existing solutions. A general overview of the prediction pipeline is also provided, covering input and output modalities, modeling features, and prediction paradigms existing in the literature. In addition, the paper discusses active research areas within trajectory prediction, addresses the posed research questions, and highlights the remaining research gaps and challenges.

References in corpus (27)

• Distilling the Knowledge in a Neural Network
• LLaMA: Open and Efficient Foundation Language Models
• Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
• BoT-SORT: Robust Associations Multi-Pedestrian Tracking
• Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
• Multi-Robot Collaborative Perception with Graph Neural Networks
• Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network
• Human Trajectory Prediction using Spatially aware Deep Attention Models
• Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
• Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks
• GAIA-1: A Generative World Model for Autonomous Driving
• MiniCPM-V: A GPT-4V Level MLLM on Your Phone
• Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
• Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving
• Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
• DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
• LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction
• Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
• Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians
• Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving
• MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction
• ALT-Pilot: Autonomous navigation with Language augmented Topometric maps
• Estimating Uncertainty For Vehicle Motion Prediction on Yandex Shifts Dataset
• An Extensible Framework for Open Heterogeneous Collaborative Perception
• V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection
• Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving
• CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus