Online Decision-Making for Scalable Autonomous Systems
Authors: Kyle Hollins Wray, Stefan J. Witwicki, Shlomo Zilberstein
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the approach in six scenarios within a realistic vehicle simulator and present its use on an AV prototype. |
| Researcher Affiliation | Collaboration | 1 College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01002 2 Nissan Research Center Silicon Valley, Sunnyvale, CA 94089 |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper evaluates the approach 'in six scenarios within a realistic vehicle simulator developed by Realtime Technologies, Inc.' It describes the scenarios in Table 1, but does not provide access information (links, DOIs, citations with authors/year for a public dataset) for any publicly available or open dataset. |
| Dataset Splits | No | The paper describes evaluation in specific scenarios but does not provide explicit training, validation, or test dataset split percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper mentions 'modest hardware' and 'fully-operational AV prototype' but does not provide specific details on CPU models, GPU models, memory, or other hardware components used for experiments. |
| Software Dependencies | No | The paper mentions using a 'realistic vehicle simulator developed by Realtime Technologies, Inc.' and the solver 'nova [Wray and Zilberstein, 2015]' but does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper describes the scenarios and baselines for evaluation but does not provide specific hyperparameter values, training configurations, or detailed system-level settings for the models or experiments. |