Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Artículo Open Access |
| Published: |
Wiley
2025
|
| Subjects: | |
| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/cav.70031 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1867003510439018496 |
|---|---|
| author | Cheng‐En Cai Sai‐Keung Wong Tzu‐Yu Chen |
| author_facet | Cheng‐En Cai Sai‐Keung Wong Tzu‐Yu Chen Cheng‐En Cai Sai‐Keung Wong Tzu‐Yu Chen |
| collection | Wiley Open Access |
| contents | Risk‐Aware Pedestrian Behavior Using Reinforcement Learning in Mixed Traffic Cheng‐En Cai Sai‐Keung Wong Tzu‐Yu Chen Computer Animation and Virtual Worlds ABSTRACTThis paper introduces a reinforcement learning method to simulate agents crossing roads in unsignalized, mixed‐traffic environments. These agents represent individual pedestrians or small groups. The method ensures that agents adopt safe interactions with nearby dynamic obstacles (bikes, motorcycles, or cars) by considering factors such as conflict zones and post‐encroachment times. Risk assessments based on interaction times encourage agents to avoid hazardous behaviors. Additionally, risk‐informed reward terms incentivize agents to perform safe actions, while collision penalties deter collisions. The method achieved collision‐free crossings and demonstrated normal, conservative, and aggressive pedestrian behaviors in various scenarios. Finally, ablation tests revealed the impact of reward weights, reward terms, and key agent state components. The weights of reward terms can be adjusted to achieve either conservative or aggressive pedestrian crossing behaviors, balancing road crossing efficiency and safety. 10.1002/cav.70031 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/cav.70031 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_cav_70031 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Risk‐Aware Pedestrian Behavior Using Reinforcement Learning in Mixed Traffic Cheng‐En Cai Sai‐Keung Wong Tzu‐Yu Chen Computer Animation and Virtual Worlds Risk‐Aware Pedestrian Behavior Using Reinforcement Learning in Mixed Traffic Cheng‐En Cai Sai‐Keung Wong Tzu‐Yu Chen Computer Animation and Virtual Worlds ABSTRACTThis paper introduces a reinforcement learning method to simulate agents crossing roads in unsignalized, mixed‐traffic environments. These agents represent individual pedestrians or small groups. The method ensures that agents adopt safe interactions with nearby dynamic obstacles (bikes, motorcycles, or cars) by considering factors such as conflict zones and post‐encroachment times. Risk assessments based on interaction times encourage agents to avoid hazardous behaviors. Additionally, risk‐informed reward terms incentivize agents to perform safe actions, while collision penalties deter collisions. The method achieved collision‐free crossings and demonstrated normal, conservative, and aggressive pedestrian behaviors in various scenarios. Finally, ablation tests revealed the impact of reward weights, reward terms, and key agent state components. The weights of reward terms can be adjusted to achieve either conservative or aggressive pedestrian crossing behaviors, balancing road crossing efficiency and safety. 10.1002/cav.70031 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Risk‐Aware Pedestrian Behavior Using Reinforcement Learning in Mixed Traffic |
| topic | Computer Animation and Virtual Worlds |
| url | https://onlinelibrary.wiley.com/doi/10.1002/cav.70031 |