# From Prediction to Planning With Goal Conditioned Lane Graph Traversals

arXiv: https://arxiv.org/abs/2302.07753 · HTML: https://mlsanity.com/paper/2302.07753/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2023.

## Built on by (most cited first, 30 of 45)

- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving](https://mlsanity.com/paper/2403.04133/) (2024)
- [Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation](https://mlsanity.com/paper/2503.12820/) (2025)
- [PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving](https://mlsanity.com/paper/2404.14327/) (2024)
- [Robust Autonomy Emerges from Self-Play](https://mlsanity.com/paper/2502.03349/) (2025)
- [Solving Motion Planning Tasks with a Scalable Generative Model](https://mlsanity.com/paper/2407.02797/) (2024)
- [Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?](https://mlsanity.com/paper/2404.07569/) (2024)
- [PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning](https://mlsanity.com/paper/2406.01587/) (2024)
- [Asynchronous Large Language Model Enhanced Planner for Autonomous Driving](https://mlsanity.com/paper/2406.14556/) (2024)
- [CaRL: Learning Scalable Planning Policies with Simple Rewards](https://mlsanity.com/paper/2504.17838/) (2025)
- [CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2502.19908/) (2025)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Rethinking Imitation-based Planner for Autonomous Driving](https://mlsanity.com/paper/2309.10443/) (2023)
- [Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling](https://mlsanity.com/paper/2505.17659/) (2025)
- [CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model](https://mlsanity.com/paper/2412.04209/) (2024)
- [Instruct Large Language Models to Drive like Humans](https://mlsanity.com/paper/2406.07296/) (2024)
- [Planning with Adaptive World Models for Autonomous Driving](https://mlsanity.com/paper/2406.10714/) (2024)
- [LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios](https://mlsanity.com/paper/2505.17209/) (2025)
- [When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks](https://mlsanity.com/paper/2510.14677/) (2025)
- [VLMPlanner: Integrating Visual Language Models with Motion Planning](https://mlsanity.com/paper/2507.20342/) (2025)
- [AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework](https://mlsanity.com/paper/2507.13729/) (2025)
- [Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving](https://mlsanity.com/paper/2403.11304/) (2024)
- [Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving](https://mlsanity.com/paper/2510.15505/) (2025)
- [nuPlan-R: A Closed-Loop Planning Benchmark for Autonomous Driving via Reactive Multi-Agent Simulation](https://mlsanity.com/paper/2511.10403/) (2025)
- [HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions](https://mlsanity.com/paper/2510.12733/) (2025)
- [LAP: Fast LAtent Diffusion Planner for Autonomous Driving](https://mlsanity.com/paper/2512.00470/) (2025)
- [DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery](https://mlsanity.com/paper/2601.10554/) (2026)
- [Utilizing Navigation Paths to Generate Target Points for Enhanced End-to-End Autonomous Driving Planning](https://mlsanity.com/paper/2406.08349/) (2024)
- [Hybrid Imitation-Learning Motion Planner for Urban Driving](https://mlsanity.com/paper/2409.02871/) (2024)
- [LHPF: Look back the History and Plan for the Future in Autonomous Driving](https://mlsanity.com/paper/2411.17253/) (2024)

## Abstract (arXiv)

The field of motion prediction for automated driving has seen tremendous progress recently, bearing ever-more mighty neural network architectures. Leveraging these powerful models bears great potential for the closely related planning task. In this letter we propose a novel goal-conditioning method and show its potential to transform a state-of-the-art prediction model into a goal-directed planner. Our key insight is that conditioning prediction on a navigation goal at the behaviour level outperforms other widely adopted methods, with the additional benefit of increased model interpretability. We train our model on a large open-source dataset and show promising performance in a comprehensive benchmark.