Baoxing Qin

Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment

This paper presents a precise localization algorithm for vehicles in 3D urban environment with only one 2D LIDAR and odometry information. A novel idea of synthetic 2D LIDAR is proposed to solve the localization problem on a virtual 2D plane. A Monte Carlo Localization scheme is adopted for vehicle position estimation, based on synthetic LIDAR measurements and odometry information. The accuracy and robustness of the proposed algorithm are demonstrated by performing real time localization in a 1.5 km driving test around the NUS campus area.

Curb-intersection feature based Monte Carlo Localization on urban roads

One of the most prominent features on an urban road is the curb, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. The combination of curb and intersection features and their idiosyncrasies carry significant information about the urban road network that can be exploited to improve a vehicles localization. This paper introduces a Monte Carlo Localization (MCL) method using the curb-intersection features on urban roads. We propose a novel idea of “Virtual LIDAR” to get the measurement models for these features. Under the MCL framework, above road observation is fused with odometry information, which is able to yield precise localization. We implement the system using a single tilted 2D LIDAR on our autonomous test bed and show robust performance in the presence of occlusion from other vehicles and pedestrians.

Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation

In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.

Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results

In this paper, we attempt to develop a reusable framework of cooperative perception for vehicle control on the road that can extend perception range beyond line-of-sight and beyond field-of-view. For this goal, the following problems are addressed: map merging, vehicle identification, sensor multi-modality, impact of communications, and impact on path planning. We provide experimental results using a self-driving vehicle and manned vehicles equipped with the cooperative perception systems that we propose and implement.

Autonomous personal vehicle for the first- and last-mile transportation services

This paper describes an autonomous vehicle testbed that aims at providing the first- and last- mile transportation services. The vehicle mainly operates in a crowded urban environment whose features can be extracted a priori. To ensure that the system is economically feasible, we take a minimalistic approach and exploit prior knowledge of the environment and the availability of the existing infrastructure such as cellular networks and traffic cameras. We present three main components of the system: pedestrian detection, localization (even in the presence of tall buildings) and navigation. The performance of each component is evaluated. Finally, we describe the role of the existing infrastructural sensors and show the improved performance of the system when they are utilized.

A general framework for road marking detection and analysis

Road markings are paintings on road surface to provide traffic guidance information for vehicles and pedestrians. In this paper, we propose a general framework for road marking detection and analysis, which is able to support various types of markings. Marking contours of different types are extracted indiscriminately from a image processing procedure, and sent to respective modules for independent classification and analysis. Four common types of markings are studied as examples in this paper, including lanes, arrows, zebra-crossings, and words. Our proposed method is tested through experiments, and shows good performance.

A Spatial-Temporal Approach for Moving Object Recognition with 2D LIDAR

Moving object recognition is one of the most fundamental functions for autonomous vehicles, which occupy an environment shared by other dynamic agents. We propose a spatial-temporal (ST) approach for moving object recognition using a 2D LIDAR. Our experiments show reliable performance. The contributions of this paper include: (i) the design of ST features for accumulated 2D LIDAR data; (ii) a real-time implementation for moving object recognition using the ST features.

Mapping with synthetic 2D LIDAR in 3D urban environment

In this paper, we report a fully automated detailed mapping of a challenging urban environment using single LIDAR. To improve scan matching, extended correlative scan matcher is proposed. Also, a Monte Carlo loop closure detection is implemented to perform place recognition efficiently. Automatic recovery of the pose graph map in the presence of false place recognition is realized through a heuristic based loop closure rejection. This mapping framework is evaluated through experiments on the real world dataset obtained from NUS campus environment.

Utilizing the infrastructure to assist autonomous vehicles in a mobility on demand context

In this paper we describe an autonomous vehicle that aims at providing shared transportation services in a mobility on demand context. As the service is limited to a known urban environment, prior knowledge of the environment can be exploited, as well as existing infrastructure sensors such as security cameras. We argue that utilizing infrastructure sensors yields greater safety of operation and allows reduction in the number of sensors required on-board, hereby reducing the cost of the vehicle. We describe the role that infrastructure sensors can play and show the resulting improved performances of the system, supported by simulation and field experiment results.

Road detection and mapping using 3D rolling window

This paper presents a method of road detection and mapping using accumulated 3D data from 2D scans. The idea of 3D rolling window is introduced, and its probabilistic characteristics are studied. A cascaded road detection process is developed with region-growing and classification methods. A probabilistic framework is utilized for road mapping purposes with the detection results. The performance of detection and mapping algorithm is evaluated through experiments.