# KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation

arXiv: https://arxiv.org/abs/2407.12940 · HTML: https://mlsanity.com/paper/2407.12940/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [MotionLM: Multi-Agent Motion Forecasting as Language Modeling](https://mlsanity.com/paper/2309.16534/) (2023)
- [Trajeglish: Traffic Modeling as Next-Token Prediction](https://mlsanity.com/paper/2312.04535/) (2023)
- [BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction](https://mlsanity.com/paper/2405.17372/) (2024)
- [SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving](https://mlsanity.com/paper/2404.06892/) (2024)
- [GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping](https://mlsanity.com/paper/2307.13204/) (2023)
- [Solving Motion Planning Tasks with a Scalable Generative Model](https://mlsanity.com/paper/2407.02797/) (2024)
- [Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07119/) (2021)
- [MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction](https://mlsanity.com/paper/2308.10280/) (2023)
- [QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2306.10508/) (2023)
- [Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023](https://mlsanity.com/paper/2306.11868/) (2023)
- [SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction](https://mlsanity.com/paper/2405.15677/) (2024)
- [Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions](https://mlsanity.com/paper/2205.10218/) (2022)
- [ControlMTR: Control-Guided Motion Transformer with Scene-Compliant Intention Points for Feasible Motion Prediction](https://mlsanity.com/paper/2404.10295/) (2024)
- [TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding](https://mlsanity.com/paper/2406.10898/) (2024)
- [Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction](https://mlsanity.com/paper/2302.10873/) (2023)
- [High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learning](https://mlsanity.com/paper/2306.07003/) (2023)
- [Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent](https://mlsanity.com/paper/2311.18307/) (2023)
- [StreamMOTP: Streaming and Unified Framework for Joint 3D Multi-Object Tracking and Trajectory Prediction](https://mlsanity.com/paper/2406.19844/) (2024)

## Built on by (most cited first, 17 of 17)

- [DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving](https://mlsanity.com/paper/2408.00415/) (2024)
- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models](https://mlsanity.com/paper/2412.05334/) (2024)
- [UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation](https://mlsanity.com/paper/2501.17015/) (2025)
- [RealEngine: Simulating Autonomous Driving in Realistic Context](https://mlsanity.com/paper/2505.16902/) (2025)
- [Enhancing Autonomous Driving Safety with Collision Scenario Integration](https://mlsanity.com/paper/2503.03957/) (2025)
- [DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling](https://mlsanity.com/paper/2503.15029/) (2025)
- [TrajTok: Technical Report for 2025 Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2506.21618/) (2025)
- [DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2510.06913/) (2025)
- [Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving](https://mlsanity.com/paper/2509.02754/) (2025)
- [Autoregressive Meta-Actions for Unified Controllable Trajectory Generation](https://mlsanity.com/paper/2505.23612/) (2025)
- [Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation](https://mlsanity.com/paper/2506.17213/) (2025)
- [Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinement](https://mlsanity.com/paper/2509.20938/) (2025)
- [SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling](https://mlsanity.com/paper/2603.04071/) (2026)
- [RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2605.19033/) (2026)
- [Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model](https://mlsanity.com/paper/2603.24989/) (2026)
- [Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation](https://mlsanity.com/paper/2607.06957/) (2026)

## Abstract (arXiv)

Trajectory generation is a pivotal task in autonomous driving. Recent studies have introduced the autoregressive paradigm, leveraging the state transition model to approximate future trajectory distributions. This paradigm closely mirrors the real-world trajectory generation process and has achieved notable success. However, its potential is limited by the ineffective representation of realistic trajectories within the redundant state space. To address this limitation, we propose the Kinematic-Driven Generative Model for Realistic Agent Simulation (KiGRAS). Instead of modeling in the state space, KiGRAS factorizes the driving scene into action probability distributions at each time step, providing a compact space to represent realistic driving patterns. By establishing physical causality from actions (cause) to trajectories (effect) through the kinematic model, KiGRAS eliminates massive redundant trajectories. All states derived from actions in the cause space are constrained to be physically feasible. Furthermore, redundant trajectories representing identical action sequences are mapped to the same representation, reflecting their underlying actions. This approach significantly reduces task complexity and ensures physical feasibility. KiGRAS achieves state-of-the-art performance in Waymo's SimAgents Challenge, ranking first on the WOMD leaderboard with significantly fewer parameters than other models. The video documentation is available at urlhttps://kigras-mach.github.io/KiGRAS/.