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

Abstract

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%.

A Dynamic Bayesian Network for Vehicle Maneuver Prediction in Highway Driving Scenarios: Framework and Verification

“…naive Bayesian approach [19] , rule-based approach [20], decision tree-based approach [21], interacting multiple model filter [22,23], hidden Markov model [24][25][26] With traffic interaction convolutional neural network [27], long short-term memory network [28][29][30], interactive hidden Markov model [31], Bayesian network and its variations [32][33][34][35][36] Physics-based features mainly refer to detective states (e.g., position, velocity, acceleration) of surrounding vehicles. Because the motion of vehicles satisfies kinematic and dynamic laws, the historical states of vehicles with kinematic and dynamic laws can be used to infer possible future states (i.e., motion boundaries) of vehicles.…”

“…Compared with the approaches which use only physics-based features, the predicting accuracy of the approach has been remarkably improved. In [20], maneuver prediction is performed based on two types of features and ontology-based rules, which models priori driving knowledge. Similarly, the paper [21] adopts the decision tree method for predicting risk behaviors in cut-in scenes.…”

A Formal Approach for the Design of a Dependable Perception System for Autonomous Vehicles

“…The recovered features are used to build an explicit representation of the world as the vehicle knows it. An ontology-based scenario description is used for knowledge representation [10]. In particular, the perception system detects obstacles, classifies them and measures their position, speed and orientation.…”

Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments

“…Some of the policies are learnt by training the driverless cars under many different road conditions. However, given the nature of uncertainties associated with driving, it is extremely difficult to exhaust all possible traffic conditions and road intersections that the autonomous vehicles will have to navigate in the real world [6].…”