A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving
This work addresses safety and efficiency for autonomous vehicles in mixed-traffic environments, but it is incremental as it builds on existing HMM techniques with specific feature enhancements.
The paper tackled the problem of predicting driving intentions of human-driven vehicles in mixed-traffic scenarios for autonomous driving safety, proposing a Hidden Markov Model (HMM) method that achieved higher prediction accuracy with continuous characterization of mobility features and improved performance by incorporating surrounding traffic.
In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Model (HMM) is proposed for autonomous vehicles. HMMs representing different driving intentions are trained and tested with field collected data from a flyover. When training the models, either discrete or continuous characterization of the mobility features of vehicles is applied. Experimental results show that the HMMs trained with the continuous characterization of mobility features can give a higher prediction accuracy when they are used for predicting driving intentions. Moreover, when the surrounding traffic of the vehicle is taken into account, the performances of the proposed prediction method are further improved.