InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers

Fu, Jiawei; Shen, Yanqing; Jian, Zhiqiang; Chen, Shitao; Xin, Jingmin; Zheng, Nanning

doi:10.1109/iros55552.2023.10342367

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

End-to-End Autonomous Driving in CARLA: A Survey

Ozaibi,

Dulva Hina,

Ramdane-Cherif

2024

IEEE Access

View full text Add to dashboard Cite

End-to-End Autonomous Driving in CARLA: A Survey

Ozaibi,

Dulva Hina,

Ramdane-Cherif

2024

IEEE Access

View full text Add to dashboard Cite

PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning

Lin,

He,

2024

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control

Gutiérrez-Moreno,

Barea,

López-Guillén

et al. 2024

Sensors

View full text Add to dashboard Cite

The use of Deep Learning algorithms in the domain of Decision Making for Autonomous Vehicles has garnered significant attention in the literature in recent years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid Decision Making module in an Autonomous Driving stack, integrating the learning capabilities from the experience of Deep Reinforcement Learning algorithms and the reliability of classical methodologies. Our Decision Making system is in charge of generating steering and velocity signals using the HD map information and sensors pre-processed data. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of Autonomous Driving modules. Specifically, the authors address the Decision Making problem by employing a Partially Observable Markov Decision Process formulation and offer a solution through the use of Deep Reinforcement Learning algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort.

show abstract

InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers

Cited by 3 publications

References 34 publications

End-to-End Autonomous Driving in CARLA: A Survey

End-to-End Autonomous Driving in CARLA: A Survey

PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning

Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control

Contact Info

Product

Resources

About