Warning

Repository archived: Data moved to Zenodo

• This repository is not maintained anymore; the data has been updated and now hosted on Zenodo at https://doi.org/10.5281/zenodo.17317056
• This repo remains an archive for consistency with older versions of the publication. For complete experiment data and up-to-date plotting scripts, please refer to the Zenodo record above.

Data for URB: Urban Routing Benchmark

Repository provides all results produced in the experiment from Urban Routing Benchmark paper. Each subdirectory of the results directory, classifies different experimental settings and includes the following scenarios and metrics.

Directory name	Experiment
scenario1	40% AVs
scenario1	40% AVs and longer training
scenario2	100% AVs
demonstrative	Demonstrative
hyperparam_search	Hyperparameter tuning
res_scenario1	Metrics for Scenario 1

Each experiment folder is named semantically (after the used network, algorithm, and random seed), and include an exp_config.json file (that documents all the parameterization used in that experiment).

Scenario1 - 40% AVs

Scenario2 - 100% AVs in St. Arnoult

Demonstrative

Selfish AVs versus adapting humans in Nangis

Malicious AVs in Nemours

Altruistic AVs in Gretz-Armainvillers

Metrics for Scenario 1

ST. ARNOULT

ALGORITHM	$t^{PRE}$	$t^{TEST}$	$t_{CAV}$	$t_{HDV}^{POST}$	$c_{ALL}$	$c_{HDV}$	$c_{CAV}$	$\Delta$ V	$\Delta$ L	WR
IPPO	3.15	3.28	3.34	3.24	0.6	0.12	1.33	-0.31	0.05	0%
IQL	3.15	3.34	3.49	3.24	0.66	0.14	1.44	-0.42	0.08	0%
MAPPO	3.15	3.32	3.43	3.25	0.66	0.14	1.45	-0.27	0.08	0%
QMIX	3.15	3.2	3.12	3.25	0.65	0.13	1.43	-0.2	0.01	66%
HUMAN	3.15	3.15	3.15	3.15	0.0	0.0	0.0	0	0.0	100%
AON	3.15	3.15	3.01	3.25	0.55	0.09	1.21	-0.06	-0.0	100%
RANDOM	3.15	3.38	3.58	3.25	0.6	0.09	1.36	-0.33	0.1	0%

PROVINS

ALGORITHM	$t^{PRE}$	$t^{TEST}$	$t_{CAV}$	$t_{HDV}^{POST}$	$c_{ALL}$	$c_{HDV}$	$c_{CAV}$	$\Delta$ V	$\Delta$ L	WR
IPPO	2.8	2.88	2.92	2.85	0.53	0.26	0.93	-0.4	0.03	0%
IQL	2.8	2.92	3.03	2.84	1.48	0.98	2.23	-0.52	0.06	0%
MAPPO	2.8	2.92	3.03	2.85	1.23	0.81	1.87	-0.64	0.06	0%
QMIX	2.8	2.96	3.14	2.85	0.88	0.54	1.41	-0.8	0.07	0%
HUMAN	2.8	2.8	2.8	2.8	0.0	0.0	0.0	0.0	0.0	100%
AON	2.8	2.81	2.76	2.84	0.47	0.19	0.99	-0.14	-0.0	100%
RANDOM	2.8	2.93	3.04	2.85	0.51	0.22	0.95	-0.62	0.06	0%

INGOLSTADT

ALGORITHM	$t^{PRE}$	$t^{TEST}$	$t_{CAV}$	$t_{HDV}^{POST}$	$c_{ALL}$	$c_{HDV}$	$c_{CAV}$	$\Delta$ V	$\Delta$ L	WR
IPPO	4.21	4.4	4.72	4.18	1.76	1.22	2.56	-0.37	0.07	0%
IQL	4.21	4.45	4.8	4.22	1.68	1.08	2.59	-0.62	0.07	0%
MAPPO	4.21	4.44	4.81	4.2	1.82	1.21	2.75	-0.62	0.07	0%
QMIX	4.21	4.36	4.55	4.23	1.2	0.67	1.98	-0.73	0.02	0%
HUMAN	4.21	4.21	4.21	4.21	0.0	0.0	0.0	0.0	0.0	100%
AON	4.21	4.29	4.37	4.23	0.87	0.55	0.24	-0.45	-0.01	0%
RANDOM	4.21	4.45	4.81	4.22	0.99	0.49	1.74	-0.68	0.07	0%

Metrics for Scenario 2

ST. ARNOULT

ALGORITHM	$t^{PRE}$	$t^{TEST}$	$t_{CAV}$	$t_{HDV}^{POST}$	$c_{ALL}$	$c_{HDV}$	$c_{CAV}$	$\Delta$ V	$\Delta$ L	WR
IPPO	3.15	3.51	3.51	N/A	1.65	0.0	1.65	-0.76	0.15	0%
IQL	3.15	3.64	3.64	N/A	0.37	0.0	0.37	-0.6	0.21	0%
MAPPO	3.15	3.65	3.65	N/A	0.65	0.0	0.65	-0.7	0.21	0%
QMIX	3.15	3.58	3.58	N/A	0.42	0.0	0.42	-0.63	0.18	0%
HUMAN	3.15	3.15	N/A	3.15	0.0	0.0	0.0	0	0.0	100%