Quoc Huy Do

Bézier curve based path planning for autonomous vehicle in urban environment

This paper presents a Bézier curve based path planner which enables the anti-collision behavior of an electronic car. The anti-collision system is a fundamental module in the architecture. The path tracking implementation uses pure pursuit algorithm. The anti-collision system based on laser scanner data consists of estimating the trajectories and behavior of surrounding objects, and a Bézier curve based path planner. Experimental results are presented showing the effectiveness of the overall navigation control system.

Unified path planner for parking an autonomous vehicle based on RRT

Maneuvering autonomous vehicles in constrained environments, such as autonomous vehicle parking, is not a trivial task and has received increasing attention from both the academy and industry. However, the traditional methods divide the problem into parallel parking, perpendicular parking, and echelon parking, then different methods are applied for the parking motion planning. In this paper a Rapidly-exploring Random Tree (RRT) based path planner is implemented for autonomous vehicle parking problem, which treats all the situations in a unified manner. As the RRT method sometimes generates some complicated paths, a smoother is also implemented for smoothing generated paths. The proposed algorithm is verified in simulation and generates applicable solutions for the proposed application scenarios.

Human Drivers Based Active-Passive Model for Automated Lane Change

Lane change maneuver is a complicated maneuver, and incorrect maneuvering is an important reason for expressway accidents and fatalities. In this scenario, automated lane change has great potential to reduce the number of accidents. Previous research in this area, typically, focuses on the generation of an optimal lane change trajectory, while ignoring the human behavior model. To understand the human lane change behavior model, we carried out experiments on Japanese expressways. By analyzing the human-driver lane change data, we propose a two-segment lane change model that mimics the human-driver. We categorize the driving environment based on the observation grid and propose different lane change behaviors to handle the different scenarios. We develop an intuitive method to select the suitable lane change behavior, for a given scenario, using active (accelerate/decelerate) and passive (wait) information derived from the distance and related velocity (dx/dv) graph. Additionally, we also identify the most desirable and safe conditions for doing lane change based on the human driver preference data. We evaluated the proposed model by performing lane change simulations in the PreScan environment, while considering the vehicle motion/control model. The simulation results show the proposed model is able to handle complicated lane change scenarios with human driver-like performance.

General behavior and motion model for automated lane change

Lane change maneuver is a cause for many severe highway accidents and automatic lane change has great potentials to reduce the impact of human error and number of accidents. Previous researches mostly tried to find an optimal trajectory and ignore the behavior model. Presented methods can be applied for simple lane change scenario and generally fail for complicated cases or in the presence of time/distance constraints. Through analysis and inspiring of human driver lane change data, we propose a multi segments lane change model to mimic the human driver for challenging scenarios. We also propose a method to convert behavior/motion selection to a time-based pattern recognition problem. We developed a simulation platform in PreScan and evaluated proposed automatic lane change method for challenging scenarios.

Vehicle path planning with maximizing safe margin for driving using Lagrange multipliers

We propose a path planning method for autonomous vehicle in cluttered environment with narrow passages. Different from traditional methods, we use a learning approach based on RBF kernel SVM to maximize the safety margin for driving. We use the Lagrange multipliers of SVM dual model to find most critical points in map and generate optimized hyperplane for path. The method is implemented on autonomous vehicle for outdoor parking and compared to well-known method in autonomous vehicle literatures. The experiments prove that the method is able to generate smooth and safe path in shorter time compared to other methods.

Narrow passage path planning using fast marching method and support vector machine

This paper introduces a novel path planning method under non-holonomic constraint for car-like vehicles, which associates map discovery and heuristic search to attain an optimal resultant path. The map discovery applies fast marching method to investigate the map geometric information. After that, the support vector machine is performed to find obstacle clearance information. This information is then used as a heuristic function which helps greatly reduce the search space. The fast marching is performed again, guided by this function to generate vehicle motions under kinematic constraints. Experimental results have shown that this method is able to generate motions for non-holonomic vehicles. In comparison with related methods, the path generated by proposed method is smoother and stay farther away from the obstacles.

Real time localization, path planning and motion control for autonomous parking in cluttered environment with narrow passages

This paper propose a novel practical method for autonomous parking in cluttered environment with narrow passages. We present a modified FastSLAM algorithm for environment mapping to reduce the map entropy and increase the localization accuracy for autonomous parking. The proposed path planning method is based on predefined arc paths for real time generation of smooth paths to avoid obstacles. The corresponding control commands are generated to minimize the steering angle control error which executed by the vehicle actuators. The proposed parking method is implemented on an autonomous vehicle platform and evaluated in the different environments with narrow passages.