# DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving

arXiv: https://arxiv.org/abs/2310.05885 · HTML: https://mlsanity.com/paper/2310.05885/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 30 of its 38 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 30 of 30)

- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients](https://mlsanity.com/paper/2109.13333/) (2021)
- [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021)
- [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2003.11476/) (2020)
- [Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving](https://mlsanity.com/paper/2109.06446/) (2021)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies](https://mlsanity.com/paper/2109.13602/) (2021)
- [Identifying Driver Interactions via Conditional Behavior Prediction](https://mlsanity.com/paper/2104.09959/) (2021)
- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles](https://mlsanity.com/paper/2212.06437/) (2022)
- [Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2006.13704/) (2020)
- [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022)
- [MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control](https://mlsanity.com/paper/2009.07517/) (2020)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Tree-structured Policy Planning with Learned Behavior Models](https://mlsanity.com/paper/2301.11902/) (2023)
- [Limitations and Improvements of the Intelligent Driver Model (IDM)](https://mlsanity.com/paper/2104.02583/) (2021)
- [Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving](https://mlsanity.com/paper/2207.03673/) (2022)
- [Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction](https://mlsanity.com/paper/2204.08665/) (2022)
- [Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving](https://mlsanity.com/paper/2302.03939/) (2023)
- [Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning](https://mlsanity.com/paper/2007.05798/) (2020)
- [Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference](https://mlsanity.com/paper/2008.08812/) (2020)

## Built on by (most cited first, 30 of 46)

- [DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba](https://mlsanity.com/paper/2408.03601/) (2024)
- [Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning](https://mlsanity.com/paper/2410.05582/) (2024)
- [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)
- [SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction](https://mlsanity.com/paper/2405.15677/) (2024)
- [Generalizing Motion Planners with Mixture of Experts for Autonomous Driving](https://mlsanity.com/paper/2410.15774/) (2024)
- [Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.09800/) (2025)
- [End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023](https://mlsanity.com/paper/2401.08658/) (2023)
- [Planning with Adaptive World Models for Autonomous Driving](https://mlsanity.com/paper/2406.10714/) (2024)
- [Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent](https://mlsanity.com/paper/2311.18307/) (2023)
- [LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios](https://mlsanity.com/paper/2505.17209/) (2025)
- [CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine](https://mlsanity.com/paper/2509.15968/) (2025)
- [When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks](https://mlsanity.com/paper/2510.14677/) (2025)
- [Fully Unified Motion Planning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2504.12667/) (2025)
- [VLMPlanner: Integrating Visual Language Models with Motion Planning](https://mlsanity.com/paper/2507.20342/) (2025)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning](https://mlsanity.com/paper/2507.04790/) (2025)
- [AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework](https://mlsanity.com/paper/2507.13729/) (2025)
- [Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving](https://mlsanity.com/paper/2605.10034/) (2026)
- [Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions](https://mlsanity.com/paper/2310.20561/) (2023)
- [LoRD: Adapting Differentiable Driving Policies to Distribution Shifts](https://mlsanity.com/paper/2410.09681/) (2024)
- [CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving](https://mlsanity.com/paper/2509.17080/) (2025)
- [Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving](https://mlsanity.com/paper/2510.15505/) (2025)
- [DAP: A Discrete-token Autoregressive Planner for Autonomous Driving](https://mlsanity.com/paper/2511.13306/) (2025)
- [Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning](https://mlsanity.com/paper/2507.12977/) (2025)
- [HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions](https://mlsanity.com/paper/2510.12733/) (2025)
- [MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving](https://mlsanity.com/paper/2512.03795/) (2025)
- [ParkDiffusion++: Ego Intention Conditioned Joint Multi-Agent Trajectory Prediction for Automated Parking using Diffusion Models](https://mlsanity.com/paper/2602.20923/) (2026)
- [Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles](https://mlsanity.com/paper/2412.01234/) (2024)

## Abstract (arXiv)

Motion prediction and cost evaluation are vital components in the decision-making system of autonomous vehicles. However, existing methods often ignore the importance of cost learning and treat them as separate modules. In this study, we employ a tree-structured policy planner and propose a differentiable joint training framework for both ego-conditioned prediction and cost models, resulting in a direct improvement of the final planning performance. For conditional prediction, we introduce a query-centric Transformer model that performs efficient ego-conditioned motion prediction. For planning cost, we propose a learnable context-aware cost function with latent interaction features, facilitating differentiable joint learning. We validate our proposed approach using the real-world nuPlan dataset and its associated planning test platform. Our framework not only matches state-of-the-art planning methods but outperforms other learning-based methods in planning quality, while operating more efficiently in terms of runtime. We show that joint training delivers significantly better performance than separate training of the two modules. Additionally, we find that tree-structured policy planning outperforms the conventional single-stage planning approach.