Thursday, 2023/03/30

  • CS/AI
  • C
  • TITECH
  • Switch Language
    • ja日本語 (Japanese)
    • enEnglish

Shimosaka Research Group

pursuing MIUBIQ (machine intelligence in UbiComp Research)

  • Home
    • Members
    • Location
  • News
  • Projects
  • Publications
  • Awards
  • Archives
    • Codes
    • Datasets
Navigation
News Presenting our paper on Robust Continuous MaxEnt IRL with RRT at IV2022

Presenting our paper on Robust Continuous MaxEnt IRL with RRT at IV2022

2022/06/09 | NewsPresentations | 376 views |

The the 33rd IEEE Intelligent Vehicles Symposium (IV22) is being held June 5th – June 9th, Aachen Germany, and online (hybrid-form). The following presentation will be delivered.

Advanced driver assistance systems have gained popularity as a safe technology that helps people avoid traffic accidents. To improve system reliability, a lot of research on driving behavior prediction has been extensively researched. Inverse reinforcement learning (IRL) is known as a prominent approach because it can directly learn complicated behaviors from expert demonstrations. Because driving data tend to have a couple of optimal behaviors from the drivers’ preferences, i.e., sub-optimality issue, maximum entropy IRL has been getting attention with their capability of considering sub-optimality.

While accurate modeling and prediction can be expected, standard maximum entropy IRL needs to calculate the partition function, which requires large computational costs. Thus, it is not straightforward to apply this model to a high-dimensional space for detailed car modeling. In addition, existing research attempts to reduce these costs by approximating maximum entropy IRL; however, a combination of the efficient path planning and the proper parameter updating is required for an accurate approximation, and existing methods have not achieved them.

In this study, we leverage a rapidly-exploring random tree (RRT) motion planner. With the RRT planner, we propose novel importance sampling for an accurate approximation from the generated trees. This ensures a stable and fast IRL model in a large high-dimensional space.

Experimental results on artificial environments show that our approach improves stability and is faster than the existing IRL methods in terms of lane change behavior prediction and left/right turn at intersection tasks.

-presentation information-
We-Po1S.10, June 8th, 9:30 – 10:50 (local time)
RRT-Based Maximum Entropy Inverse Reinforcement Learning for Robust and Efficient Driving Behavior Prediction
Shinpei Hosoma, Masato Sugasaki, Hiroaki Arie, and Masamichi Shimosaka

  • tweet

Comments are disabled for this post

Social Networks

  • twitter
  • rss

Recent News

  • Device-Free Multi-Person Indoor Localization Using the Change of ToF 2023/03/03
  • Presenting our paper on Device-Free Multi-Person Indoor Localization Using the Change of ToF at PerCom2023 2023/02/28
  • Our paper on Efficient Adaptive Beacon Deployment Optimization for Indoor Crowd Monitoring has been published in IMWUT. 2023/01/24
  • Presenting our paper on Robust Continuous MaxEnt IRL with RRT at IV2022 2022/06/09
  • Presenting our paper on Efficient Indoor Localization Model Construction by Sequential Recommendation of Data Gathering Position based on Bayesian Optimization at IPIN2021 2021/11/29
  • Adaptive incremental beacon placement optimization for crowd density monitoring applications 2021/11/01
  • Presenting 2 papers at ACM SIGSPATIAL 2021 2021/11/01
  • Fine-grained Urban Dynamics Prediction using Large-Scale Mobile Phone Location Data 2021/10/05
  • Robustifying Wi-Fi localization by Between-Location data augmentation 2021/09/28
  • Our paper on robustifying Wi-Fi localization by “Between-Location” data augmentation has been published in IEEE Sensors Journal 2021/09/17

Search

Copyright 2015 · Shimosaka Research Group at TITECH