$DBO$ Trajectory Planning and $HAHP$ Decision-Making for Autonomous Vehicle Driving on Urban Environment

Abstract

A novel driving behaviour oriented (DBO) trajectory planner and hierarchical analytic hierarchy process (HAHP) decision maker are presented for intelligent vehicle. Since driving on structural road should satisfy actuator constraints and improve comfortableness as soon as possible, which strictly obeys traffic rules other than making traffic mess, it is rather than purely pursuing the shortest route/time. By analysis traffic rules, theDBO framework is employed to produce trajectories. To make trajectory drivable, cubic B-spline and clothoid curve are modeled to keep continuous curvature, and cubic polynomial curve is to schedule velocity profile satisfying stability and comfort. To pick out the best trajectory, HAHP decision maker is developed to evaluate the candidates. The first layer selects optimal paths considering smoothness and economy, and the second layer selects best trajectory taking smoothness, comfortableness and economy in account. Moreover, DBO rapidly exploring random tree (RRT) replanner is embedded to ensure algorithm completeness. Finally, several typical scenarios are designed to verify the real-time and reliability of the algorithm. The results illustrate that the algorithm has highly real-time and stability evaluated by Statistical Process Control method as the probability for the peak time less than 0.1s is 100% except three obstacles avoidance scenario is 59.31% in 1000 cycles. Since the planned trajectory is smooth enough and satisfy the constraints of the actuator, the mean lateral tracking error is less than 0.2m with 0.5m peak error, and the mean speed error less than 0.5km/h with 1.5km/h peak error for all scenarios.

Intelligent Distribution Framework and Algorithms for Connected Logistics Vehicles

“…This error is prominent in the S bend, as shown in Figure 16. The main causes of this phenomenon are mechanical reaction lag and overshoot, and the error is also within 13 cm, which is in controllable range [38].…”

State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment

“…As the resulting path does not satisfy non-holonomic constraints, C. Zhang et al [9] proposed trajectory planning and tracking for autonomous vehicles, based on a state lattice and model predictive control. To find feasible continuous plans, D. Zeng et al [10] employed smooth cubic curvature polynomials in their investigation to ensure algorithm completeness and pick out the best trajectory, taking smoothness, comfort and economy into account. In the field of mobile robotics, navigation is an essential task classified into global navigation and local navigation.…”