A Hierarchical Control System for Autonomous Driving towards Urban Challenges

Abstract

In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. The experimental results show that the proposed framework can operate efficiently in the urban scenario.

Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments

“…Rearrange the error states in term of dynamic Equation (13) with the input state vector of Equation (2). Finally, the covariance matrix of Equation (13) propagate from t γ to t γ+1 computed as [35,36]:…”

“…In recent years, the field of robotics has witnessed remarkable advances in both academia and the industry with the assistance of Artificial Intelligence (AI). The evolution of technology has awakened various real-time applications of the mobile robot, such as search and rescue missions in disasters, ship and deliver small packages, and self-driving vehicles [1,2]. The localization system of an autonomous mobile robot is a crucial competence.…”

Safety aspects of critical scenario identification for autonomous transport

“…Our study is based on the basic concept of connected transport systems, which assumes that transport system components located close to each other in space and time can form an ad-hoc network The components of these networks collect, receive, and transmit information about their own state, perceived environmental characteristics, and other system users (Dinh Van et al, 2020). Of course, in addition to vehicles, other road users (such as pedestrians) can connect to the network.…”

Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

“…In particular, the linear reference model output (LRMO) tracking control setting is advantageous, ensuring indirect state-feedback linearization of control systems. Such linearity property of control systems is critical for higherlevel learning paradigms such as Iterative Learning Control [28][29][30][31][32][33][34] and primitive-based learning [34][35][36][37][38][39][40], as representative hierarchical learning control paradigms [41][42][43][44].…”