DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation | Paper Citation Record

Observation d0fa6d51-5a9a-4177-9463-41336f242c0f · outbound

This paper cites Flow: A modular learning framework for mixed autonomy traffic,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Flow: A modular learning framework for mixed autonomy traffic,

Reference 1

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Observation cc312bdf-a040-4b90-8dd4-7bef2eabc814 · outbound

This paper cites SceneDiffuser: Efficient and controllable driving simulation initialization and rollout,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation SceneDiffuser: Efficient and controllable driving simulation initialization and rollout,

Reference 2

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Observation 9e4f1ab6-24a8-4150-ae88-98b5449e1120 · outbound

This paper cites TrafficMCTS: A closed-loop traffic flow generation framework with group-based monte carlo tree search,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation TrafficMCTS: A closed-loop traffic flow generation framework with group-based monte carlo tree search,

Reference 3

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Observation e7474161-c2c7-4c96-8377-2b65e4822a8d · outbound

This paper cites Transferring causal driving patterns for generalizable traffic simulation with diffusion-based distillation,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Transferring causal driving patterns for generalizable traffic simulation with diffusion-based distillation,

Reference 4

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Observation 5998458b-d78f-4a1e-b456-24968c94cb10 · outbound

This paper cites Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: A game theoretic approach,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: A game theoretic approach,

Reference 5

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Observation b54b6fa3-038e-4866-972a-2d2429944774 · outbound

This paper cites Hybrid system stability analysis of multilane mixed-autonomy traffic,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Hybrid system stability analysis of multilane mixed-autonomy traffic,

Reference 6

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Observation c0089b47-0a60-4691-bd5f-a42677ad9262 · outbound

This paper cites En- hancing safety in mixed traffic: Learning-based modeling and efficient control of autonomous and human-driven vehicles,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation En- hancing safety in mixed traffic: Learning-based modeling and efficient control of autonomous and human-driven vehicles,

Reference 7

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Observation c0ed872f-b6d0-4283-b236-8dcd3f2f68c2 · outbound

This paper cites Study on traffic flows with connected vehicles and human-driven vehicles,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Study on traffic flows with connected vehicles and human-driven vehicles,

Reference 8

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Observation 65ba1803-bc79-442c-84b1-2f4b1af4961a · outbound

This paper cites Energy and environmental implications of automated vehicles under mixed autonomy traffic environment,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Energy and environmental implications of automated vehicles under mixed autonomy traffic environment,

Reference 9

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Observation b75653a3-e0a2-438e-98ae-70901f2f8ccc · outbound

This paper cites Analysis of roadway capacity for heterogeneous traffic flows considering the degree of trust of drivers of HVs in CA Vs,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Analysis of roadway capacity for heterogeneous traffic flows considering the degree of trust of drivers of HVs in CA Vs,

Reference 10

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Observation ce1e1aef-a857-4dd0-9244-6ac204c0abf4 · outbound

This paper cites Exploring the impact of conditionally automated driving vehicles transferring control to human drivers on the stability of heterogeneous traffic flow,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Exploring the impact of conditionally automated driving vehicles transferring control to human drivers on the stability of heterogeneous traffic flow,

Reference 11

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Observation 7c23055c-2b0b-4492-8043-0bb559bf1ffb · outbound

This paper cites Learning to control and coordi- nate mixed traffic through robot vehicles at complex and unsignalized intersections,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Learning to control and coordi- nate mixed traffic through robot vehicles at complex and unsignalized intersections,

Reference 12

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Observation 17fb2b10-9ba8-496c-beff-28fb59fd1e25 · outbound

This paper cites Modeling and robustH inf tycontrol synthesis of the CA V-HDV heterogeneous traffic system with different car-following modes,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Modeling and robustH inf tycontrol synthesis of the CA V-HDV heterogeneous traffic system with different car-following modes,

Reference 13

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Observation 91d6ffe6-b317-4a57-ab79-41c93fa5d856 · outbound

This paper cites Urban vehicle trajectory generation based on generative adversarial imitation learning,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Urban vehicle trajectory generation based on generative adversarial imitation learning,

Reference 14

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Observation 970ff2ba-15fc-4bfe-bada-87eae3c32c70 · outbound

This paper cites Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning

Reference 15

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Observation c9d08bdb-ac5c-4131-b410-4c05d8264559 · outbound

This paper cites DiffAIL: Diffusion adversarial imitation learning,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation DiffAIL: Diffusion adversarial imitation learning,

Reference 16

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Observation 9a6f6795-61a1-40b8-a182-b2922a50ed82 · outbound

This paper cites A fast and stable framework for generative adversarial imitation learning,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation A fast and stable framework for generative adversarial imitation learning,

Reference 17

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Observation 6e64bcc3-0690-41d7-94f6-c50eb2d64d1c · outbound

This paper cites Gen- eralizable multi-modal adversarial imitation learning for non-stationary dynamics,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Gen- eralizable multi-modal adversarial imitation learning for non-stationary dynamics,

Reference 18

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Observation 09ec19e7-b14e-42b5-9f69-f92a05654a13 · outbound

This paper cites ControlTraj: Controllable trajectory generation with topology-constrained diffusion model,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation ControlTraj: Controllable trajectory generation with topology-constrained diffusion model,

Reference 19

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Observation c749ede9-72b2-4a45-ad23-d74890427cd8 · outbound

This paper cites Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation

Reference 20

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Observation 73a18378-a414-46ef-954b-6833875eea16 · outbound

This paper cites Diffusion-based planning for autonomous driving with flexible guidance,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Diffusion-based planning for autonomous driving with flexible guidance,

Reference 21

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Observation d24d758d-9187-42aa-9e7a-6f9e7265f9ee · outbound

This paper cites Context-aware trajectory prediction for autonomous driving in heterogeneous environments,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Context-aware trajectory prediction for autonomous driving in heterogeneous environments,

Reference 22

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Observation 6aa9b831-59cf-48c5-a51b-2c36c269aced · outbound

This paper cites Interaction- aware and driving style-aware trajectory prediction for heterogeneous vehicles in mixed traffic environment,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Interaction- aware and driving style-aware trajectory prediction for heterogeneous vehicles in mixed traffic environment,

Reference 23

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Observation 1fedb42d-e869-4440-93ac-fa56d163e929 · outbound

This paper cites Post-interactive multimodal trajectory prediction for autonomous driving,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Post-interactive multimodal trajectory prediction for autonomous driving,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T13:46:57.614535Z

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Observation 8e7c6f38-d124-4481-bda2-4bb1749d5872 · outbound

This paper cites Multi-agent reinforcement learning with transformer-based spatio-temporal fusion for autonomous driving in mixed traffic,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Multi-agent reinforcement learning with transformer-based spatio-temporal fusion for autonomous driving in mixed traffic,

Reference 25

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Observation eaa62028-c2f0-49d2-9469-1ab2eba02243 · outbound

This paper cites A planning-oriented autonomous driving framework: From image to trajectory with intent-aware prediction,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation A planning-oriented autonomous driving framework: From image to trajectory with intent-aware prediction,

Reference 26

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Observation 42d47c93-f410-42ac-b0ab-bc36e62cc8d4 · outbound

This paper cites DragTraffic: Interactive and controllable traffic scene generation for autonomous driving,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation DragTraffic: Interactive and controllable traffic scene generation for autonomous driving,

Reference 27

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Observation 315bfd54-0c97-41af-bbf6-172dc7895b6c · outbound

This paper cites LD-Scene: LLM- guided diffusion for controllable generation of adversarial safety-critical driving scenarios,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation LD-Scene: LLM- guided diffusion for controllable generation of adversarial safety-critical driving scenarios,

Reference 28

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Observation 4f86ae25-36fb-44c4-b5f2-dde698b0891e · outbound

This paper cites Modelling two-dimensional driving behaviours at unsignalised intersection using multi-agent imitation learning,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Modelling two-dimensional driving behaviours at unsignalised intersection using multi-agent imitation learning,

Reference 29

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Observation b15751b2-e58c-4413-b3a9-2a99d7437c7f · outbound

This paper cites DiffScene: Diffusion-based safety-critical scenario generation for autonomous vehicles,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation DiffScene: Diffusion-based safety-critical scenario generation for autonomous vehicles,

Reference 30

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Observation 7b718378-4ef1-489f-b11f-f68fd0b9bf87 · outbound

This paper cites SceneControl: Diffusion for controllable traffic scene generation,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation SceneControl: Diffusion for controllable traffic scene generation,

Reference 31

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Observation 123c1632-b993-4f35-8900-8bbd2fdf3eec · outbound

This paper cites Optimizing diffu- sion models for joint trajectory prediction and controllable generation,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Optimizing diffu- sion models for joint trajectory prediction and controllable generation,

Reference 32

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Observation baad8269-ae2f-43be-88a0-87ed4082ef70 · outbound

This paper cites Intention-aware denoising diffusion model for trajectory prediction,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Intention-aware denoising diffusion model for trajectory prediction,

Reference 33

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Observation 99a2b533-b7be-4493-a084-f7355003db3c · outbound

This paper cites Traffic flow impact of mixed heterogeneous platoons on highways: An approach combining driving simulation and microscopic traffic simulation,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Traffic flow impact of mixed heterogeneous platoons on highways: An approach combining driving simulation and microscopic traffic simulation,

Reference 34

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Observation 95aa3c59-876f-4b49-bb83-3b50dc2dfbf3 · outbound

This paper cites A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning,

Reference 35

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Observation a6a4d99e-19d1-467a-99ec-c6abc76386ee · outbound

This paper cites An efficient high-risk lane-changing scenario edge cases generation method for autonomous vehicle safety testing,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation An efficient high-risk lane-changing scenario edge cases generation method for autonomous vehicle safety testing,

Reference 36

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Observation ebcd8609-a9ef-4bdd-ab87-e56ce4f1685c · outbound

This paper cites Detection and analysis of corner case scenarios at a signalized urban intersection,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Detection and analysis of corner case scenarios at a signalized urban intersection,

Reference 37

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Observation 1e96e223-1b42-41d6-91ac-48e9eff01c9e · outbound

This paper cites Application of uncertainty to out-of-distribution detection for autonomous driving perception safety,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Application of uncertainty to out-of-distribution detection for autonomous driving perception safety,

Reference 38

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Observation 3cd3f8aa-4fce-40eb-a71b-4f8e32bb47a9 · outbound

This paper cites Optimization of conditional value- at-risk,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Optimization of conditional value- at-risk,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-12T13:46:57.614535Z

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Observation b042f6cd-5050-433b-9c96-5d9760770425 · outbound

This paper cites Denoising diffusion probabilistic models,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Denoising diffusion probabilistic models,

Reference 40

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Observation 2b901152-f170-4f5d-8455-cdaf2d1a5877 · outbound

This paper cites Improved denoising diffusion proba- bilistic models,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Improved denoising diffusion proba- bilistic models,

Reference 41

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Observation 30d02af0-651b-45df-a04b-e2af44ace05b · outbound

This paper cites Generative adversarial imitation learning,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Generative adversarial imitation learning,

Reference 42

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Observation ec2ca699-fb4d-47fa-8732-ec1742965697 · outbound

This paper cites Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,

Reference 43

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Observation 111c9d29-f558-422c-a629-1cf763f1b36e · outbound

This paper cites The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-12T13:46:57.614535Z

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Observation fd461d33-0c35-416a-8dc1-a3465596f088 · outbound

This paper cites The round dataset: A drone dataset of road user trajectories at roundabouts in germany,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation The round dataset: A drone dataset of road user trajectories at roundabouts in germany,

Reference 45

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Observation 8d9568f3-385c-4e3a-b946-96e029e02c25 · outbound

This paper cites The exid dataset: A real-world trajectory dataset of highly interactive highway scenarios in germany,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation The exid dataset: A real-world trajectory dataset of highly interactive highway scenarios in germany,

Reference 46

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Observation f777032d-1e9e-4272-aabb-0f7f428549ce · outbound

This paper cites The ind dataset: A drone dataset of naturalistic road user trajectories at german intersections,.

DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation The ind dataset: A drone dataset of naturalistic road user trajectories at german intersections,

Reference 47

Resolution: malformed identifier; no resolver link, observed 2026-07-12T13:46:57.614535Z

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