Predictions of short-term driving intention using recurrent neural network on sequential data
This addresses the challenge of enabling autonomous vehicles to reason about driver behaviors, which is crucial for safe planning and decision-making.
The paper tackles the problem of predicting short-term driving intentions for autonomous vehicles, demonstrating a methodology to build and predictive system that learns on-road characteristics like risks and aggressiveness.
Predictions of driver's intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the fundamental units that constitute more sophisticated driving goals, behaviors, such as overtaking the slow vehicle in front, exit or merge onto a high way, etc. While it is not uncommon that most of the time human driver can rationalize, in advance, various on-road behaviors, intentions, as well as the associated risks, aggressiveness, reciprocity characteristics, etc., such reasoning skills can be challenging and difficult for an autonomous driving system to learn. In this article, we demonstrate a disciplined methodology that can be used to build and train a predictive drive system, therefore to learn the on-road characteristics aforementioned.