Paper Citation Record · LEDGER

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty

As of 23 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2605.20255.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.20255 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T18:41:15.177901Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact18
verified fuzzy13
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e6f7f818-db96-4631-ab2c-b433ce6d3487 · outbound

This paper cites Simulation of pedestrian interaction with autonomous vehicles via social force model,.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Simulation of pedestrian interaction with autonomous vehicles via social force model,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.993980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 04564f4f-9649-4a5d-89d2-ff6f8064e81b · outbound

This paper cites Available: https://www.sciencedirect.com/science/ article/pii/S1569190X24000157.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Available: https://www.sciencedirect.com/science/ article/pii/S1569190X24000157

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.991803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fd939498-59f0-48dd-a134-8017540af49e · outbound

This paper cites How Does Simulation-Based Testing for Self- Driving Cars Match Human Perception?.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty How Does Simulation-Based Testing for Self- Driving Cars Match Human Perception?

Reference 3

Resolution: verified exact; doi, observed 2026-06-30T18:44:59.532112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation be97785d-93bd-4af5-a6e9-9a1debf70a61 · outbound

This paper cites The surprising effectiveness of PPO in cooperative multi- agent games,.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty The surprising effectiveness of PPO in cooperative multi- agent games,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.995892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fedb83ed-24e1-41d8-bd4e-a48797a9ba91 · outbound

This paper cites Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.969370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 121efac0-34e8-4da8-be1e-97e1ba41cdb2 · outbound

This paper cites Social Force Model for Pedestrian Dynamics.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Social Force Model for Pedestrian Dynamics

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-30T18:45:00.591975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 35dea3e1-b8d1-4f9d-96ac-34c6e194343a · outbound

This paper cites Will automated vehicles encourage more jaywalking? Results from a stated preference survey,.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Will automated vehicles encourage more jaywalking? Results from a stated preference survey,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.982834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76c00bd6-b307-46a4-ad63-5fe951fe88dc · outbound

This paper cites Multi-Agent Reinforcement Learning-based Cooperative Autonomous Driving in Smart Intersections.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Multi-Agent Reinforcement Learning-based Cooperative Autonomous Driving in Smart Intersections

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:45:00.594721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f6c51587-10bc-4c23-a66e-83ca402a9ef1 · outbound

This paper cites Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:45:00.598336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b395c8d0-e4ee-44af-9429-e6038e4a3dcf · outbound

This paper cites Multi- agent actor-critic for mixed cooperative-competitive environments.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Multi- agent actor-critic for mixed cooperative-competitive environments

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:44:59.522886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e90d3f0e-0e0d-4623-b1f1-59e2fef0d4b0 · outbound

This paper cites Monotonic value function factorisation for deep multi-agent reinforcement learning.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Monotonic value function factorisation for deep multi-agent reinforcement learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:44:59.526412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fd537707-f395-47ec-822d-9a7013737d79 · outbound

This paper cites Counterfactual multi-agent policy gradients.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Counterfactual multi-agent policy gradients

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:44:59.520942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e3754cdb-3ace-4344-8be2-5eb1a42be905 · outbound

This paper cites Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T18:45:00.623817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a7ff6c15-e561-40da-a0c9-16f34cf8ea24 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Proximal Policy Optimization Algorithms

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-30T18:45:00.626776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a73aaf74-6b65-435c-b5b4-3990920b593b · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-06-30T18:45:00.617600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1c3869a5-dd55-49db-a33b-37f23a5a092a · outbound

This paper cites A concise introduction to decentralized POMDPs, volume 1.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty A concise introduction to decentralized POMDPs, volume 1

Reference 16

Resolution: verified exact; doi, observed 2026-06-30T18:44:59.516088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8bc23dee-59c8-4ce7-bf32-2ca00b7c8ae8 · outbound

This paper cites Fuzzy Logic- Based Model That Incorporates Personality Traits for Heterogeneous Pedestrians.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Fuzzy Logic- Based Model That Incorporates Personality Traits for Heterogeneous Pedestrians

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.984860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5c759112-6fd0-4d05-b19e-07570317fbea · outbound

This paper cites Modeling Pedestrian Crossing Behavior: A Reinforcement Learning Approach with Sensory Motor Constraints.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Modeling Pedestrian Crossing Behavior: A Reinforcement Learning Approach with Sensory Motor Constraints

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:45:00.620896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb2d2526-6136-4e84-8bf9-d9aa5918e325 · outbound

This paper cites Social LSTM: Human Trajectory Prediction in Crowded Spaces,.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Social LSTM: Human Trajectory Prediction in Crowded Spaces,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.978683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 51029e86-be5d-4756-8d0c-f650fd6bdfea · outbound

This paper cites Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:45:00.611548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9e10f171-2f64-4c2a-9cab-4e69b43868f7 · outbound

This paper cites Impact of jaywalking on pedestrian interaction behavior: A multiagent Markov Game- based analysis,.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Impact of jaywalking on pedestrian interaction behavior: A multiagent Markov Game- based analysis,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.973541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b0a0ab91-ce04-4598-b838-69ad55d03f64 · outbound

This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-06-30T18:45:00.614529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 518a4289-7111-4b7f-be5b-fec6b108d169 · outbound

This paper cites Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:45:00.619576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ffe394e2-764d-4d5a-9f19-cff0a03233f0 · outbound

This paper cites Uncertainty Quantification for Safe and Reliable Autonomous Vehicles: A Review of Methods and Applications.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Uncertainty Quantification for Safe and Reliable Autonomous Vehicles: A Review of Methods and Applications

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:45:00.623375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76876e59-f0ce-418b-9c03-87123a90b0f4 · outbound

This paper cites CARLA: An Open Urban Driving Simulator.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty CARLA: An Open Urban Driving Simulator

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-06-30T18:45:00.615758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 57050906-dbea-48c6-b0a1-ecbabb4668fc · outbound

This paper cites Microscopic Traffic Simulation using SUMO.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Microscopic Traffic Simulation using SUMO

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:44:59.529962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1a085232-fd68-4e74-86e8-7c6f1ce2595c · outbound

This paper cites JAX: composable transformations of Python+NumPy programs.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty JAX: composable transformations of Python+NumPy programs

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.976130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 484bde7f-451f-4547-aabe-3ef1e0edc9c8 · outbound

This paper cites JaxMARL: Multi-Agent RL Environments and Algorithms in JAX.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:02.673620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 40e925ac-5abe-4e1e-b6c4-e9ae63bc09c7 · outbound

This paper cites CleanRL: High-quality Single- file Implementations of Deep Reinforcement Learning Algorithms,.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty CleanRL: High-quality Single- file Implementations of Deep Reinforcement Learning Algorithms,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.980649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b573feae-8908-4a7d-a3fd-91acbf1881f6 · outbound

This paper cites Available: http://jmlr.org/papers/v23/21-1342.html.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Available: http://jmlr.org/papers/v23/21-1342.html

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.989710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ae9ac56-35db-4d3f-98c5-e140bfd3f869 · outbound

This paper cites The kinematic bicycle model: A consistent model for planning feasible tra- jectories for autonomous vehicles?.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty The kinematic bicycle model: A consistent model for planning feasible tra- jectories for autonomous vehicles?

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.986983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 00dd8e5b-868f-4914-a05f-a19406593214 · outbound

This paper cites Physics-informed machine learning for precision unmanned aerial vehicle control: Adaptive transformers with safety guarantees,.

Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty Physics-informed machine learning for precision unmanned aerial vehicle control: Adaptive transformers with safety guarantees,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T03:34:32.971444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.