# IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning arXiv: https://arxiv.org/abs/2311.02850 ยท HTML: https://mlsanity.com/paper/2311.02850/ ## Key facts - Cited by 2 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 27 of its 56 references that are in the map. - First posted on arXiv 6 Nov 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, 27 of 27) - [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) - [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021) - [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021) - [Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals](https://mlsanity.com/paper/2106.15004/) (2021) - [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021) - [M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction](https://mlsanity.com/paper/2202.11884/) (2022) - [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2003.11476/) (2020) - [Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking](https://mlsanity.com/paper/2102.09117/) (2021) - [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022) - [Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving](https://mlsanity.com/paper/1905.00229/) (2019) - [Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models](https://mlsanity.com/paper/2204.02392/) (2022) - [Social Interactions for Autonomous Driving: A Review and Perspectives](https://mlsanity.com/paper/2208.07541/) (2022) - [Interactive multi-modal motion planning with Branch Model Predictive Control](https://mlsanity.com/paper/2109.05128/) (2021) - [Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation](https://mlsanity.com/paper/2207.05853/) (2022) - [Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor](https://mlsanity.com/paper/1906.09788/) (2019) - [GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging](https://mlsanity.com/paper/2109.01896/) (2021) - [Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning](https://mlsanity.com/paper/1904.02390/) (2019) - [Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement](https://mlsanity.com/paper/2205.11853/) (2022) - [Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections](https://mlsanity.com/paper/1904.05423/) (2019) - [Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving](https://mlsanity.com/paper/2011.00462/) (2020) - [Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction](https://mlsanity.com/paper/2203.15112/) (2022) - [Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation](https://mlsanity.com/paper/1910.07141/) (2019) - [Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching](https://mlsanity.com/paper/2003.02746/) (2020) - [Safe Planning for Self-Driving Via Adaptive Constrained ILQR](https://mlsanity.com/paper/2003.02757/) (2020) - [Optimizing Trajectories with Closed-Loop Dynamic SQP](https://mlsanity.com/paper/2109.07081/) (2021) - [Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model](https://mlsanity.com/paper/2209.09013/) (2022) ## Built on by (most cited first, 2 of 2) - [CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving](https://mlsanity.com/paper/2509.17080/) (2025) - [LHPF: Look back the History and Plan for the Future in Autonomous Driving](https://mlsanity.com/paper/2411.17253/) (2024) ## Abstract (arXiv) Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless, acquiring an interactive and secure trajectory for the ADS remains challenging due to the complex nature of interaction modeling in planning. Modern planning methods still employ a uniform treatment of prediction outcomes and solely rely on collision-avoidance strategies, leading to suboptimal planning performance. To address this limitation, this paper presents a novel prediction-based interactive planning framework for autonomous driving. Our method incorporates interaction reasoning into spatio-temporal (s-t) planning by defining interaction conditions and constraints. Specifically, it records and continually updates interaction relations for each planned state throughout the forward search. We assess the performance of our approach alongside state-of-the-art methods in the CommonRoad environment. Our experiments include a total of 232 scenarios, with variations in the accuracy of prediction outcomes, modality, and degrees of planner aggressiveness. The experimental findings demonstrate the effectiveness and robustness of our method. It leads to a reduction of collision times by approximately 17.6% in 3-modal scenarios, along with improvements of nearly 7.6% in distance completeness and 31.7% in the fail rate in single-modal scenarios. For the community's reference, our code is accessible at https://github.com/ChenYingbing/IR-STP-Planner.