Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning
- Resource Type
- Conference
- Authors
- Mandal, Sampurna; Biswas, Swagatam; Balas, Valentina E.; Shaw, Rabindra Nath; Ghosh, Ankush
- Source
- 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Computing Communication and Automation (ICCCA), 2020 IEEE 5th International Conference on. :768-773 Oct, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Deep learning
Computational modeling
Transportation
Predictive models
Feature extraction
Autonomous automobiles
Autonomous vehicles
Autonomous Vehicles (AV)
Deep Learning (DL)
Artificial Intelligence (AI)
- Language
- ISSN
- 2642-7354
Autonomous Vehicles are expected to change the future of worldwide transportation system. As self-driving cars are facing a lot of engineering challenges, it is one of the hottest topics in recent research. One such challenge is to build models to predict the movements of traffic agents such as cars, cyclists, pedestrians etc around the self-driving cars. The objective of this paper is to analyse the prediction efficiency of various deep learning models by calculating root mean square error score. This deep learning models takes a current state of the surrounding and depending on that predict the motion for the agents.