A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios

Abstract

Many variants of the Rapidly-exploring Random Tree (RRT) algorithm use biased-sampling strategies for solving computationally intensive tasks. One of such tasks is the planning of safe trajectories with the simultaneous intervention in both the longitudinal and the lateral dynamics of the vehicle in complex traffic-scenarios with multiple static and dynamic objects. A recently proposed hybrid statistical learning approach uses a 3D convolutional neural network (3D-ConvNet) to predict suitable longitudinal acceleration profiles in combination with an RRT variant called the Augmented CL-RRT algorithm. This algorithm is not effective in complex traffic-scenarios, i.e., traffic scenarios with more than 4 dynamic objects, because of the lack of flexibility and biasing in the longitudinal and the lateral dynamics intervention, respectively. Therefore, an extension to the Augmented CL-RRT algorithm is introduced to improve the longitudinal dynamics intervention with actuator and stable profile constraints and named as the Augmented CL-RRT+ algorithm. A biased-sampling strategy is also proposed based on the predicted longitudinal acceleration and steering wheel angle profiles provided by a trained 3D-ConvNet. Simulations are performed to compare different trajectory planning algorithms based on efficiency and safety. The results show vast improvements in terms of the efficiency without harming the safety.