Learning Human Driving Behaviors with Sequential Causal Imitation Learning
Authors: Kangrui Ruan, Xuan Di4583-4592
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our methods are evaluated on a synthetic dataset and a real-world highway driving dataset, both demonstrating that the proposed procedure significantly outperforms noncausal imitation learning methods.We demonstrate the superiority of our proposed method over non-causal baselines by conducting experiments on a car-following dataset and a real-world highway dataset. |
| Researcher Affiliation | Academia | Kangrui Ruan, Xuan Di Columbia University, New York, NY, USA kr2910@columbia.edu, sharon.di@columbia.edu |
| Pseudocode | Yes | Algorithm 1: Finding π(a|scausal) by GAIL |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | The public NGSIM data records the real-world human driving trajectories for US Highway 101, and Interstate 80 Freeway (Alexiadis et al. 2004). |
| Dataset Splits | No | The paper mentions using a 'synthetic dataset' and a 'real-world highway driving dataset, Next Generation Simulation (NGSIM)' and that 'expert demonstrations are generated by running PPO-clip'. However, it does not provide specific percentages, sample counts, or explicit instructions for how the datasets were split into training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., library names, frameworks, or programming language versions like 'Python 3.8' or 'PyTorch 1.9'). |
| Experiment Setup | No | The paper describes the overall experimental approach, including the use of GAIL and PPO, and mentions 'ϵ is a hyper-parameter to control the clip value' in the PPO objective. However, it does not provide concrete numerical values for key hyperparameters such as learning rate, batch size, number of epochs, or other optimizer settings necessary for explicit reproduction of the experimental setup. |