cs.AIApr 14, 2026
SaveArtificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
Organizations: Department of Transport & Planning, Delft University of Technology, Delft, the Netherlands. · Department of Industrial and Systems Engineering, University of Michigan, Dearborn, Michigan, USA · 3Tandon School of Engineering, New York University, New York, United States.
Abstract
Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment for evaluating AV performance in varied conditions. However, existing simulation tools mainly focus on graphical realism and rely on simple rule-based models and therefore fail to accurately represent the complexity of driving behaviors and interactions. Artificial intelligence (AI) has shown strong potential to address these limitations; however, despite the rapid progress across AI methodologies, a comprehensive survey of their application to mixed autonomy traffic simulation remains lacking. Existing surveys either focus on simulation tools without examining the AI methods behind them, or cover ego-centric decision-making without addressing the broader challenge of modeling surrounding traffic. Moreover, they do not offer a unified taxonomy of AI methods covering individual behavior modeling to full scene simulation. To address these gaps, this survey provides a structured review and synthesis of AI methods for modeling AV and human driving behavior in mixed autonomy traffic simulation. We introduce a taxonomy that organizes methods into three families: agent-level behavior models, environment-level simulation methods, and cognitive and physics-informed methods. The survey analyzes how existing simulation platforms fall short of the needs of mixed autonomy research and outlines directions to narrow this gap. It also provides a chronological overview of AI methods and reviews evaluation protocols and metrics, simulation tools, and datasets. By covering both traffic engineering and computer science perspectives, we aim to bridge the gap between these two communities.
Figures & tables
Fig. 1: Survey methodology, scope, and structure
Fig. 2: Taxonomy of AI methods for modeling mixed-autonomy traffic
| Survey | Mixed Autonomy Focus | Traffic Agent Model. | Multi-Agent Methods | World Models | Scenario Generation | Cognitive / Physics | Sim. Tools & Datasets | Cross- Discipl. |
| Schwarting et al. [ 22 ] | – | (✓) | – | – | – | – | – | – |
| Kuutti et al. [ 27 ] | – | – | – | – | – | – | ✓ | – |
| Grigorescu et al. [ 28 ] | – | (✓) | – | – | – | – | (✓) | – |
| Chao et al. [ 31 ] | (✓) | ✓ | (✓) | – | – | – | ✓ | (✓) |
| Di and Shi [ 21 ] | ✓ | – | (✓) | (✓) | – | (✓) | – | ✓ |
| Kiran et al. [ 29 ] | – | – | – | – | – | – | (✓) | – |
TABLE I: Comparison of this survey with related surveys across key dimensions. ✓ = covered; (✓) = partially covered; – = not covered.
| Method | Training Signal | Loop | Strengths | Limitations | Representative Studies |
| Imitation Learning / Behavior Cloning | Expert demonstrations (supervised) | OL, CL | Simple and scalable; direct data-to-policy mapping; strong baselines with large datasets | Covariate shift and compounding errors; causal confusion; limited robustness to out-of-distribution states | ALVINN [ 14 ] , PilotNet [ 15 ] , CIL [ 16 ] , ChauffeurNet [ 34 ] , DAgger [ 35 ] , TransFuser [ 36 ] , Urban Driver [ 37 ] |
| Trajectory Prediction | Logged trajectories (supervised) | OL | Multi-modal forecasting; mature standardized benchmarks; captures distributional structure of future motion | Marginal prediction; non-reactive; displacement metrics miss interactive quality | CoverNet [ 38 ] , MultiPath [ 39 ] , TNT [ 40 ] , DenseTNT [ 41 ] , LaPred [ 42 ] , PRIME [ 43 ] , MID [ 12 ] |
| Reinforcement Learning | Reward signal (trial-and-error) | CL | Discovers novel strategies via exploration; no expert demonstrations needed; inherently reactive | Sample inefficiency; reward engineering difficulty; safety during exploration; sim-to-real gap | Kendall et al. [ 18 ] , Roach [ 44 ] , BC-SAC [ 45 ] , Think2Drive [ 46 ] , HAIM-DRL [ 47 ] , Safe RL [ 17 ] |
| IRL / Adversarial Imitation | Expert demos (adversarial matching) | CL | Recovers interpretable reward functions; robust to compounding errors; captures driving preferences | Training instability; mode collapse; sensitivity to discriminator design; scaling difficulty | MaxEnt IRL [ 48 ] , GAIL [ 49 ] , Kuefler et al. [ 50 ] , PS-GAIL [ 51 ] , AIRL [ 52 ] , RAIL [ 53 ] |
| End-to-End Learned Planners | Expert demos + perception labels | OL, CL | Joint perception–planning; end-to-end gradient flow; unified architecture | Ego-status bias; open-loop metric gap; limited closed-loop generalization | NMP [ 54 ] , UniAD [ 55 ] , VAD [ 56 ] , SparseDrive [ 57 ] , InterFuser [ 58 ] , ThinkTwice [ 59 ] , NAVSIM [ 60 ] |
| Model-Based Policy Learning | Learned world model + reward | CL | Sample-efficient via imagination; decouples dynamics learning from policy optimization | Model bias; compounding errors in imagined rollouts; sensitive to model accuracy | World Models [ 61 ] , Dreamer [ 62 ] , DreamerV3 [ 63 ] , MILE [ 64 ] , AdaWM [ 65 ] , Imagine-2-Drive [ 66 ] |
TABLE II: Summary of single-agent behavior modeling methods for mixed autonomy traffic simulation.
| Method | Core Idea | Strengths | Limitations | Representative Studies |
| Joint Trajectory Forecasting (predominantly open-loop) | ||||
| Joint Trajectory Prediction | Predicts joint future trajectories for multiple agents; uses graph networks, attention, or transformers to model inter-agent dependencies | Captures inter-agent dependencies; multi-modal outputs; strong benchmark performance; joint consistency | Does not model reactive feedback; joint consistency not guaranteed under marginal losses; gap to interactive simulation | Social LSTM [ 88 ] , Social GAN [ 89 ] , DESIRE [ 90 ] , Trajectron++ [ 91 ] , PRECOG [ 92 ] , VectorNet [ 93 ] , LaneGCN [ 94 ] , Scene Transformer [ 95 ] , HiVT [ 96 ] , MTR++ [ 97 ] , GameFormer [ 98 ] |
| Centralized Neural Simulation (closed-loop) | ||||
| Diffusion-Based Simulators | Models multi-agent evolution via iterative denoising; samples from learned joint trajectory distributions with guidance for controllability | High diversity; multi-modal generation; controllable via guidance; collision-aware sampling | Slow sampling (iterative denoising); hard constraint satisfaction difficult; closed-loop stability over long horizons | MotionDiffuser [ 13 ] , CTG [ 99 ] , CTG++ [ 100 ] |
| Token-Based / Autoregressive | Discretizes motion into tokens; casts simulation as conditional language modeling with next-token prediction | Scalable transformer architectures; handles variable agent counts; real-time inference possible | Discretization error; compounding token mistakes; serialization order asymmetry; constraint satisfaction requires constrained decoding | MotionLM [ 101 ] , Trajeglish [ 102 ] , BehaviorGPT [ 103 ] , SMART [ 104 ] |
| Regression-Based Reactive | Single learned model produces globally consistent scene updates via continuous regression; rolls forward step-by-step | Global scene consistency; captures multi-agent dependencies; computationally efficient; responds to ego deviations | Long-horizon drift; limited multi-modality (point estimates); counterfactual validity concerns from passive logs | SimNet [ 105 ] , TrafficSim [ 106 ] |
TABLE III: Summary of multi-agent interaction methods for mixed autonomy traffic simulation.
| Method | Output Space | Core Idea | Strengths | Limitations | Representative Studies |
| Generative World Models | |||||
| Video-Based & Latent World Models | Multi-view images; latent tokens; video frames | Learns action-conditioned video or latent generation via autoregressive token prediction or latent diffusion; produces future visual observations for end-to-end evaluation | Photorealistic rollouts; end-to-end stack evaluation; controllable via actions, text, and structured inputs; emergent scene dynamics | Long-horizon drift; high computational cost; causal consistency under intervention not guaranteed; image quality metrics miss driving-critical correctness | GAIA-1 [ 138 ] , GAIA-2 [ 19 ] , Drive-WM [ 139 ] , DriveDreamer [ 140 ] , DriveDreamer-2 [ 141 ] , Vista [ 142 ] , Copilot4D [ 143 ] , MUVO [ 144 ] , DrivingWorld [ 145 ] , GenAD [ 146 ] |
| Occupancy-Based World Models | 3D/4D semantic occupancy grids; BEV fields | Predicts future semantic occupancy conditioned on ego actions via autoregressive or diffusion generation in voxel or triplane space | Geometric and semantic structure; directly supports planning cost evaluation; captures free-space and drivable-area evolution | No agent identity or intent; compounding topology errors; memory-intensive voxel grids; expensive 4D labels | OccWorld [ 147 ] , OccSora [ 148 ] , DOME [ 149 ] , T 3 Former [ 150 ] , -World [ 151 ] , Drive-OccWorld [ 152 ] , OccLLaMA [ 153 ] |
| Traffic Scenario Generation | |||||
| Scene Initialization & Scenario Synthesis | Static scene layouts; multi-agent trajectories; map + traffic configurations | Generates traffic configurations ranging from static actor placements to full dynamic scenarios; controllable methods expose control interfaces (language, temporal logic, guidance functions, retrieval tags) for steerable generation | Data-driven scene diversity; user-specified controllability; intuitive language interfaces; compositional control; supports scenario augmentation and targeted testing | Realism–control trade-off; stronger constraints push off data manifold; unstandardized control interfaces; scene initialization methods lack dynamic validation; limited closed-loop validation | SceneGen [ 154 ] , TrafficGen [ 155 ] , DriveSceneGen [ 156 ] , CTG [ 99 ] , CTG++ [ 100 ] , Scenario Diffusion [ 157 ] , LCTGen [ 158 ] , ChatSim [ 159 ] , RealGen [ 160 ] |
| Safety-Critical Generation & Adversarial Falsification | Scenario parameters; adversarial agent trajectories; safety-critical multi-agent configurations | Discovers failure-inducing scenarios via optimization-based search (RL adversaries, importance sampling, fuzzing) or safety-targeted generative synthesis (guided diffusion, latent-space optimization with learned traffic priors) | Efficient failure discovery; targeted stress testing; identifies safety-critical edge cases; generative methods maintain realism via learned priors; complements naturalistic evaluation | Validity–criticality trade-off; adversarial drift to implausible scenarios; coverage gaps; failure finding risk estimation; guided generation provides soft not hard constraint satisfaction | AST [ 161 , 162 ] , Accelerated Eval. [ 163 ] , STRIVE [ 164 ] , KING [ 165 ] , AdvDiffuser [ 166 ] , CAT [ 167 ] , AV-FUZZER [ 168 ] , CCDiff [ 169 ] , CaDRE [ 170 ] , BridgeGen [ 171 ] , MADS [ 172 ] |
TABLE IV: Summary of neural simulation environment and scene generation methods for mixed autonomy traffic simulation.
| Method | Theory Basis | Core Idea | Strengths | Limitations | Representative Studies |
| Bounded Rationality and Decision-Making Models | |||||
| Prospect-Theoretic Decision Models | Cumulative Prospect Theory (CPT); Prospect Balancing | Replaces expected utility with S-shaped value function and nonlinear probability weighting; captures loss aversion and overweighting of rare catastrophic outcomes in driving decisions | Interpretable style variation via CPT parameters; reproduces conservative/aggressive profiles without separate rewards; psychologically validated | Parameter calibration from driving data is difficult; limited to single-decision settings; does not model temporal dynamics of choices | Tversky & Kahneman [ 181 ] , Schmidt et al. [ 182 ] , Sun et al. [ 183 ] |
| Risk Allostasis & Homeostatic Models | Risk Allostasis Theory; Task-Capability Interface | Drivers adjust behavior to maintain a subjective target risk level rather than optimizing utility; explains systematic deviations from time-optimal behavior | High fidelity to naturalistic car-following and speed choice; captures risk compensation effects; simple parameterization | Target risk level is latent and hard to estimate; limited to longitudinal control; does not explain strategic interaction | Fuller [ 184 ] , Mohammadian et al. [ 185 ] , Kashifi [ 186 ] |
| Drift-Diffusion Models (DDMs) | Sequential Sampling; Evidence Accumulation | Decision variable accumulates noisy perceptual evidence toward boundaries; models the temporal dynamics and variability of gap acceptance, braking, and yielding decisions | Captures full RT distribution, including dangerous late decisions; perceptual inputs (looming, TTC) as natural drift rates; differentiable neural implementations | Binary decisions only; limited multi-alternative extensions; requires careful specification of evidence signals; calibration from naturalistic data is challenging | Ratcliff [ 187 ] , Markkula et al. [ 188 ] , Zgonnikov et al. [ 189 ] , McDonald et al. [ 190 ] , Fengler et al. [ 191 ] |
| Level- Reasoning | Cognitive Hierarchy; Behavioral Game Theory | Agents have heterogeneous strategic depth: level-0 follows heuristics, level- best-responds to level- ; models realistic variation in anticipatory reasoning | Principled heterogeneity in interactive behavior; avoids full-rationality assumption; calibratable from observed interactions | Requires specifying level-0 heuristic; distribution over levels is scenario-dependent; computational cost grows with | Stahl & Wilson [ 117 ] , Chong et al. [ 118 ] , Li et al. [ 192 ] |
| Cognitive Architectures, Attention, Risk Perception, and Trust Models | |||||
TABLE V: Summary of cognitive and physics-informed AI methods for mixed autonomy traffic simulation.
Fig. 3: Timeline of model development in driving simulation and virtual evaluation
| Dataset | Location(s) | Year | Duration / Scenarios | Freq. (Hz) | Environment | Agent Types | Key Features |
| Drone-Based Trajectory Datasets | |||||||
| NGSIM [ 227 ] | CA, GA (USA) | 2005–06 | 90 min | 10 | Highway, arterial | Vehicles | Foundational; car-following analysis |
| highD [ 226 ] | Germany | 2018 | 16.5 h / 110k vehicles | 25 | Highway (6 sites) | Cars, trucks | High precision ( 10 cm); 5,600 lane changes |
| inD [ 228 ] | Germany | 2020 | 10 h / 11.5k road users | 25 | Urban intersections (4 sites) | Vehicles, cyclists, pedestrians | Naturalistic intersection behavior |
| rounD [ 229 ] | Germany | 2020 | 6 h / 13k road users | 25 | Roundabouts (3 sites) | Vehicles, cyclists, pedestrians | Complex interaction patterns |
| pNEUMA [ 231 ] | Athens (Greece) | 2018 | 12.5 h / 0.5M trajectories | 25 | Urban CBD (congested) | Cars, taxis, buses, PTWs, medium/heavy vehicles | Swarm of 10 drones over 5 days; dense multimodal trajectories over large urban network |
TABLE VI: Major publicly available datasets for trajectory prediction, behavior modeling, and planning in automated driving. Duration indicates total recording time; scenarios indicates number of extracted segments for benchmarking where applicable.
| Tool | Primary Application | Sim Level | Supported Datasets | Background Agents | Throughput |
| State-Level / Data-Driven Simulators | |||||
| GPUDrive | Multi-agent RL training | State | WOMD | Learned policies, rule-based | Very high (GPU-native, 1M+ steps/s) |
| Waymax | Motion planning, prediction evaluation | State | WOMD | Log replay, IDM, learned | High (JAX/GPU/TPU batching) |
| nuPlan Devkit | Planning benchmark and evaluation | State | nuPlan | IDM, log replay, learned (SMART) | Moderate (multi-process) |
| Nocturne | Multi-agent RL research | State | WOMD-derived scenarios | Log replay, learned (BC baseline) | High (C++ core, 2k steps/s) |
| highway-env | RL algorithm prototyping | State | Synthetic (procedural) | IDM, MOBIL | High (vectorized environments) |
TABLE VII: Simulation tools commonly used with learning-based driving methods. Simulation level indicates output granularity: sensor (camera/LiDAR images), state (bounding boxes, trajectories), or traffic (aggregate flow). Background agents refers to how non-ego vehicles are modeled during simulation.