Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Advanced Technologies Group (United States)
Abstract
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there still remains more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for this is high uncertainty of traffic behavior and large number of situations that an SDV may encounter on the roads,…
Citation impact
- FWCI
- 40.42
- Percentile
- 100%
- References
- 62
Authors
8- HCHenggang CuiCorresponding
Advanced Technologies Group (United States)
- VRVladan Radosavljević
Advanced Technologies Group (United States)
- FCFang‐Chieh Chou
Advanced Technologies Group (United States)
- TLTsung-Han Lin
Advanced Technologies Group (United States)
- TNThi Nguyen
Advanced Technologies Group (United States)
Topics & keywords
- Computer science
- Raster graphics
- Artificial intelligence
- Task (project management)
- Context (archaeology)
- Robotics
- Convolutional neural network
- Deep learning