PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment | Paper Citation Record

Observation 8237d5ab-e979-4299-b654-1360e40e0a20 · outbound

This paper cites Waymo’s incidents: As of the end of 2024, the national highway traffic safety administration (nhtsa) had received 835 reports documenting 696 incidents involving waymo vehicles.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Waymo’s incidents: As of the end of 2024, the national highway traffic safety administration (nhtsa) had received 835 reports documenting 696 incidents involving waymo vehicles

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.068564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60e9da36-3ec3-4c8f-ac7f-0b52cd417a9f · outbound

This paper cites As of 2024, there have been 51 reported fatalities involving tesla’s autopilot function, with 44 verified by nhtsa or expert testimony.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment As of 2024, there have been 51 reported fatalities involving tesla’s autopilot function, with 44 verified by nhtsa or expert testimony

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.031981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 16adfc39-edd4-4078-b0cb-0942d7d5dd84 · outbound

This paper cites Performance limit evaluation strategy for automated driving systems.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Performance limit evaluation strategy for automated driving systems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.080159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 17454915-18e5-47c1-a322-44195d98e583 · outbound

This paper cites Risk and complexity assessment of autonomous vehicle testing scenarios.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Risk and complexity assessment of autonomous vehicle testing scenarios

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.002082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 23349652-c8d0-4897-930d-923830bf5c2d · outbound

This paper cites A survey on datasets for the decision making of autonomous vehicles.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment A survey on datasets for the decision making of autonomous vehicles

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:54.988038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 683f6714-a1d0-4380-8234-dab1dcfafdd1 · outbound

This paper cites A survey on safety-critical driving scenario generation—a methodological perspec- tive.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment A survey on safety-critical driving scenario generation—a methodological perspec- tive

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.041958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b8e9789-7223-419b-b219-13dc2b87e646 · outbound

This paper cites Generative Modeling for Adversarial Lane-Change Scenarios.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Generative Modeling for Adversarial Lane-Change Scenarios

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.612941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 52cbe665-a0b7-4bef-bec6-aab9411a9851 · outbound

This paper cites Kinetic analysis and numerical tests of an adaptive car-following model for real-time traffic in its.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Kinetic analysis and numerical tests of an adaptive car-following model for real-time traffic in its

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.007545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f2a2924d-3438-453b-a676-f41c6f6811ba · outbound

This paper cites Advsim: Generating safety-critical scenarios for self- driving vehicles.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Advsim: Generating safety-critical scenarios for self- driving vehicles

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.011423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a7b7a603-d7d2-4229-91a1-a61b44926125 · outbound

This paper cites Trafficgen: Learning to generate diverse and realistic traffic scenarios.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Trafficgen: Learning to generate diverse and realistic traffic scenarios

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.054638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8774a37e-d6a5-498d-bf86-20300c4a6bef · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment LLaMA: Open and Efficient Foundation Language Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:18:54.637158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5341ed57-5886-45d5-9ed1-9015387c5e6a · outbound

This paper cites INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.610001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21e6ada7-e45c-4cee-9448-302fc1cae605 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:18:54.619274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4995bd05-0fdc-4cc9-88b2-5b433b807cc7 · outbound

This paper cites DeepSeek-V3 Technical Report.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment DeepSeek-V3 Technical Report

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:18:54.628313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 025c5f2e-aa03-4f31-9093-9262d26b86fd · outbound

This paper cites Qwen2.5 Technical Report.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Qwen2.5 Technical Report

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:18:54.616307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c66cde10-fea1-41c0-aeee-f869bcded9fc · outbound

This paper cites Language conditioned traffic generation.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Language conditioned traffic generation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.003833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6165da49-4d00-4c1d-991c-67f5d8581d09 · outbound

This paper cites Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.066596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e8eaf50f-2372-4452-8ab7-73269bbc032b · outbound

This paper cites Surfelgan: Synthesizing realistic sensor data for autonomous driving.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Surfelgan: Synthesizing realistic sensor data for autonomous driving

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.036173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7d816d11-8832-4a96-b640-fcc61586aa85 · outbound

This paper cites Interactive critical scenario generation for autonomous vehicles testing based on in-depth crash data using reinforcement learning.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Interactive critical scenario generation for autonomous vehicles testing based on in-depth crash data using reinforcement learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.070438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5f566847-bf75-4f12-a095-6b1f3a187003 · outbound

This paper cites Adaptive stress testing of airborne collision avoidance systems.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Adaptive stress testing of airborne collision avoidance systems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.062886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f6efff3b-1052-4bc0-a3db-c07e24006478 · outbound

This paper cites Generating adversarial driving scenarios in high-fidelity simulators.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Generating adversarial driving scenarios in high-fidelity simulators

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.052757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 113581a0-c7a6-4bc3-b68d-e39778c3516b · outbound

This paper cites Structured domain randomization: Bridging the reality gap by context-aware synthetic data.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Structured domain randomization: Bridging the reality gap by context-aware synthetic data

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.050965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4097a700-98bb-4f8c-bf33-3e9eaa133d31 · outbound

This paper cites Adversarial safety-critical scenario generation using naturalistic human driving priors.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Adversarial safety-critical scenario generation using naturalistic human driving priors

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.061050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a045f8e-9390-43f3-a361-a20348598f8a · outbound

This paper cites King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.074629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f20814db-9e4f-475d-a2d5-be3f848d0bbd · outbound

This paper cites LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.643336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 684b69cc-493c-447a-9a70-29848e998204 · outbound

This paper cites FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.646301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a9ae4fbb-0fa6-4eb6-8c47-009bed21d0bf · outbound

This paper cites Ontology based scene creation for the development of automated vehicles.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Ontology based scene creation for the development of automated vehicles

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.016700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ea9af57-f95b-41a2-a5f6-6cadb82dfde8 · outbound

This paper cites Summit: A simulator for urban driving in massive mixed traffic.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Summit: A simulator for urban driving in massive mixed traffic

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.047558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 39030f91-b12c-4af1-b37b-cd4ae4b671f8 · outbound

This paper cites Scenario factory: Creating safety-critical traffic scenarios for automated vehicles.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Scenario factory: Creating safety-critical traffic scenarios for automated vehicles

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.059018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 224a11b9-e7dd-44c6-a089-3145f7cc4ddf · outbound

This paper cites TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:19:49.341731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2faa46a5-5dcc-4474-b2a1-5b458190e393 · outbound

This paper cites LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:18:54.625566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 150711df-82df-43fc-98c1-4a8f04b54ac2 · outbound

This paper cites Optimizing autonomous driving for safety: A human-centric approach with llm-enhanced rlhf.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Optimizing autonomous driving for safety: A human-centric approach with llm-enhanced rlhf

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.039960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b32448d6-fba0-43a3-ab49-16cea7bd2b53 · outbound

This paper cites Chatscene: Knowledge-enabled safety- critical scenario generation for autonomous vehicles.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Chatscene: Knowledge-enabled safety- critical scenario generation for autonomous vehicles

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.029895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0ee9255c-efb2-42d1-8c9b-c030fb17a63e · outbound

This paper cites Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.640361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1c71aa65-e701-4432-bd11-2f6ba3de743b · outbound

This paper cites Promptable Closed-loop Traffic Simulation.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Promptable Closed-loop Traffic Simulation

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.631524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec3d3fd8-e185-4349-894c-b4a03efaae9e · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:18:54.634269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation df61a42f-ef4f-4f84-8240-ca1a92bb9b8a · outbound

This paper cites Billion-scale similarity search with gpus.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Billion-scale similarity search with gpus

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.005775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8bb46921-4725-46c3-a193-db0a2af36823 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:18:54.603842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7b295016-b34c-46ad-ac40-6b0713b1a61a · outbound

This paper cites The primacy bias in deep reinforcement learning.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment The primacy bias in deep reinforcement learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.038073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60c117bb-fdad-404a-9e46-fbda5afcd808 · outbound

This paper cites Cat: Closed-loop adversarial training for safe end-to-end driving.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Cat: Closed-loop adversarial training for safe end-to-end driving

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.076660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a7dbc24d-b602-46c5-be47-37d3cf97cf85 · outbound

This paper cites Low-Dissipation Data Bus via Coherent Quantum Dynamics.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Low-Dissipation Data Bus via Coherent Quantum Dynamics

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.607178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cae28779-32d2-4633-a9e3-3b6201222b4e · outbound

This paper cites Gpt-5 technical overview.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Gpt-5 technical overview

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.043772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a461551e-8fd0-488d-a582-287ad54a9b1d · outbound

This paper cites Claude 4 model family.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Claude 4 model family

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.064590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 95a30233-7b98-421b-9154-05cbffff90cf · outbound

This paper cites CARLA: An open urban driving simulator.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment CARLA: An open urban driving simulator

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.020393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4bcf2767-bec3-42b9-9cc8-d8ebfc782387 · outbound

This paper cites ego vehicle going straight with background vehi- cle turning left.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment ego vehicle going straight with background vehi- cle turning left

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:54.994627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0924529e-88a3-4d08-aa2d-d626bfd2db2f · outbound

This paper cites The integration process is designed as follows: a) Data-driven Insight Extraction.:The ego vehicle’s motion patterns are summarized by parsing the processed JSON trajectory data.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment The integration process is designed as follows: a) Data-driven Insight Extraction.:The ego vehicle’s motion patterns are summarized by parsing the processed JSON trajectory data

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:54.990449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 26002ca1-e119-4e3e-8a71-cdb60c7f86a3 · outbound

This paper cites turn left.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment turn left

Reference 47

Resolution: malformed identifier; raw_fallback, observed 2026-05-20T19:18:54.998579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 585a6783-7963-493a-8003-6edbbf04db21 · outbound

This paper cites an unresolved cited work.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-05-20T19:18:55.018496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e33cd1ea-d281-48c4-b360-e43c7ef63f51 · outbound

This paper cites Semantic consistency must be strictly maintained—no changes to the original intent are allowed.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Semantic consistency must be strictly maintained—no changes to the original intent are allowed

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.027541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ffb81405-56ac-431b-9bbc-37e1ccdd39b3 · outbound

This paper cites It is not necessary to specify the full road network structure; instead, provide a template that references reading from an .osm map.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment It is not necessary to specify the full road network structure; instead, provide a template that references reading from an .osm map

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:54.992687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1c8481dc-e2d7-42d6-9498-7722607c8c43 · outbound

This paper cites Each element in the DSL must have a clear one-to-one semantic correspondence to the narrative description.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Each element in the DSL must have a clear one-to-one semantic correspondence to the narrative description

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.022743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1cd51561-7b8f-4760-9696-4511a8afb8c9 · outbound

This paper cites description.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment description

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.014888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0ee5eebc-870b-4641-bb56-f0919521711a · outbound

This paper cites an unresolved cited work.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-05-20T19:18:55.000217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7d8095af-202b-487e-bfd4-6fa99bbf5a3b · outbound

This paper cites When creating scenario representations, please prioritize choosing from existing elements.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment When creating scenario representations, please prioritize choosing from existing elements

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:54.996712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e06579fe-ecd4-45d4-a349-a14c9ddc09e8 · outbound

This paper cites •Syntax alignment checking: a) Ensure semantic consistency between the scenario description and generated scenario representation.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment •Syntax alignment checking: a) Ensure semantic consistency between the scenario description and generated scenario representation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.056947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 832b02b0-95bd-4cfe-adec-132d961aefd6 · outbound

This paper cites (generated geometry snippet).

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment (generated geometry snippet)

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.078420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a3a24c9-30c4-4f8d-bf61-aa15945025fe · outbound

This paper cites The overall workflow for DSL-to-Python code generation, including retrieval, prompt construction, and iterative debugging, is illustrated in Figure 8.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment The overall workflow for DSL-to-Python code generation, including retrieval, prompt construction, and iterative debugging, is illustrated in Figure 8

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.072456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c43d1ded-6027-4372-9879-7d919b374140 · outbound

This paper cites DSL code block b.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment DSL code block b

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.025449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 13b3d88f-1454-4352-a00e-2c927e26dd45 · outbound

This paper cites an unresolved cited work.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-05-20T19:18:55.013122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4aa7c928-2a2c-437f-a722-77c635ca49e8 · outbound

This paper cites an unresolved cited work.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-20T19:18:55.049188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 04591f0b-b914-499c-858a-6083b7b51866 · outbound

This paper cites open- closed.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment open- closed

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.033980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9815cb23-43d7-427f-b2e9-777acc7a9bb5 · outbound

This paper cites 2)Instructions for Code Generation: •Preserve all logic encoded in the DSL behaviors without truncation.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment 2)Instructions for Code Generation: •Preserve all logic encoded in the DSL behaviors without truncation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.045724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation df9bf61e-3a82-4db5-8fb7-2979122f0c89 · outbound

This paper cites 4)DSL Embedding: •Include the full DSL content within the prompt, properly formatted as a code block.

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment 4)DSL Embedding: •Include the full DSL content within the prompt, properly formatted as a code block

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:18:55.009367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers