Autonomous Path Planning for Road Vehicles in Narrow Environments: An Efficient Continuous Curvature Approach

Abstract

In this paper we introduce a novel method for obtaining good quality paths for autonomous road vehicles (e.g., cars or buses) in narrow environments. There are many traffic situations in urban scenarios where nontrivial maneuvering in narrow places is necessary. Navigating in cluttered parking lots or having to avoid obstacles blocking the way and finding a detour even in narrow streets are challenging, especially if the vehicle has large dimensions like a bus. We present a combined approximation-based approach to solve the path planning problem in such situations. Our approach consists of a global planner which generates a preliminary path consisting of straight and turning-in-place primitives and a local planner which is used to make the preliminary path feasible to car-like vehicles. The approximation methodology is well known in the literature; however, both components proposed in this paper differ from existing similar planning methods. The approximation process with the proposed local planner is proven to be convergent for any preliminary global paths. The resulting path has continuous curvature which renders our method well suited for application on real vehicles. Simulation experiments show that the proposed method outperforms similar approaches in terms of path quality in complicated planning tasks.

Visibility-Based Technologies and Methodologies for Autonomous Driving

“…Extensive research has been conducted for path planning for automated parking, which is now a reality in many cities [34][35][36][37][38]. Automated parking aims to eliminate the influence of human factors, improve the quality and accuracy of control, and reduce the maneuver time by optimizing vehicle path in restricted parking zones.…”

Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed

“…To obtain the Jacobian, similar to Eqs. (8)(9)(10)(11)(12)(13), the linearization of Eq. (39) is first obtained by letting, Then, the matrix A (7×7) has the following nonzero elements:…”

“…For a passenger car, the only parameter is the longitudinal wheelbase L which equals 2.8 m. Eqs. (7)(8)(9)(10)(11)(12)(13)(14) are used for model prediction. For the parking maneuver (Figure 3, 4a, b), the quadratic linear form of the cost function works well while optimizing without restrictions when the minimum of a cost function is absolute.…”

Thai Voice-Controlled Analysis for Car Parking Assistance in System-on-Chip Architecture

“…Hence, to improve the parking skills of the driver, Yukawa [4] proposed using the reverse parking direction through the auditory assistance approach. Several studies have also proposed parking assist systems, such as an autonomous path planning in the narrow environments [5], a surround-view parking system [6], and a voice-controlled embedded system for parking a prototype car [7][8]. The need to minimize drive disruption in the operation of the car led by Honda [9] use analog control to assist the driver in operating the car more confidently.…”