A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

By Xinli Geng, Huawei Liang, Biao Yu, Pan Zhao, Li He, Rulin Huang. 2017

83 citations recorded by the source corpus.

Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles’ driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario’s characteristics. Finally, the target vehicle’s future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.

Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Building and Construction

In Control and Systems Engineering

Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “In [210], a target vehicle’s future behavior was predicted with a hidden Markov model (HMM) and the prediction time horizon was extended 56% by learning human driving traits.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “get vehicle’s future behavior and detect unsafe lane change events [133], [134].”