Dynamic Conditional Imitation Learning for Autonomous Driving
Author's Department
Computer Science & Engineering Department
Find in your Library
http://arxiv.org/pdf/2211.11579
Document Type
Research Article
Publication Title
IEEE Transactions on Intelligent Transportation Systems
Publication Date
Fall 12-1-2022
doi
https://doi.org/10.1109/TITS.2022.3214079
Abstract
Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner, to mimic human driving. This approach has demonstrated suitable vehicle control when following roads, avoiding obstacles, or taking specific turns at intersections to reach a destination. Unfortunately, performance dramatically decreases when deployed to unseen environments and is inconsistent against varying weather conditions. Most importantly, the current CIL fails to avoid static road blockages. In this work, we propose a solution to those deficiencies. First, we fuse the laser scanner with the regular camera streams, at the features level, to overcome the generalization and consistency challenges. Second, we introduce a new efficient Occupancy Grid Mapping (OGM) method along with new algorithms for road blockages avoidance and global route planning. Consequently, our proposed method dynamically detects partial and full road blockages, and guides the controlled vehicle to another route to reach the destination. Following the original CIL work, we demonstrated the effectiveness of our proposal on CARLA simulator urban driving benchmark. Our experiments showed that our model improved consistency against weather conditions by four times and autonomous driving success rate generalization by 52%. Furthermore, our global route planner improved the driving success rate by 37%. Our proposed road blockages avoidance algorithm improved the driving success rate by 27%. Finally, the average kilometers traveled before a collision with a static object increased by 1.5 times. The main source code can be reached at this web page: https://heshameraqi.github.io/dynamic_cil_autonomous_driving .
First Page
22988
Last Page
23001
Recommended Citation
H. M. Eraqi, M. N. Moustafa and J. Honer, "Dynamic Conditional Imitation Learning for Autonomous Driving," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 22988-23001, Dec. 2022, doi: 10.1109/TITS.2022.3214079.