Xiaotong Shen

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 golf cars for public trial of mobility-on-demand service

We detail the design of autonomous golf cars which were used in public trials in Singapores Chinese and Japanese Gardens, for the purpose of raising public awareness and gaining user acceptance of autonomous vehicles. The golf cars were designed to be robust, reliable, and safe, while operating under prolonged durations. Considerations that went in to the overall system design included the fact that any member of the public had to not only be able to easily use the system, but to also not have the option to use the system in an unintended manner. This paper details the hardware and software components of the golf cars with these considerations, and also how the booking system and mission planner facilitated users to book for a golf car from any of ten stations within the gardens. We show that the vehicles performed robustly throughout the prolonged operations with a small localization variance, and that users were very receptive from the user survey results.

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.

Autonomous vehicle planning system design under perception limitation in pedestrian environment

Autonomous driving within the pedestrian environment is always challenging, as the perception ability is limited by the crowdedness and the planning process is constrained by the complicated human behaviors. In this paper, we present a vehicle planning system for self-driving with limited perception in the pedestrian environment. Acknowledging the difficulty of obstacle detection and tracking within the crowded pedestrian environment, only the raw LIDAR sensing data is employed for the purpose of traversability analysis and vehicle planning. The designed vehicle planning system has been experimentally validated to be robust and safe within the populated pedestrian environment.

Motion planning using cooperative perception on urban road

In this paper, we consider motion planning with long-range sensing information provided by cooperative perception. Firstly, we develop a general framework to reflect sensing uncertainty and transmission delay into motion planning. The Bayesian filter is utilized for perception belief fusion, which is then formulated into a cost function for optimal planning. With the cost map, we leverage the optimal property of RRT* framework and propose a long-term perspective planning algorithm to exploit the benefits introduced by long-range sensing. Finally, we demonstrate our proposed methods for a self-driving vehicle featured with cooperative perception. The experiment result shows that the proposed approach is able to improve the planning performance and is applicable to real-time implementation.

Spatio-temporal motion features for laser-based moving objects detection and tracking.

This paper proposes a spatio-temporal motion feature detection and tracking method using range sensors working on a moving platform. The proposed spatio-temporal motion features are similar to optical flow but are extended on a moving platform with fusion of odometry and show much better classification accuracy with consideration of different uncertainties. In the proposal, the ego motion is compensated by odometry sensors and the laser scan points are accumulated and represented as space-time point clouds, from which the velocities and moving directions can be extracted. Based on these spatio-temporal features, a supervised learning technique is applied to classify the points as static or moving and Kalman filters are implemented to track the moving objects. A real experiment is performed during day and night on an autonomous vehicle platform and shows promising results in a crowded and dynamic environment.

Scalable Cooperative Localization with Minimal Sensor Configuration

Localization of distributed robots can be improved by fusing the sensor data from each robot collectively in the network. This may allow for each individual robot’s sensor configuration to be reduced while maintaining an acceptable level of uncertainty. However, the scalability of a reduced sensor configuration should be carefully considered lest the propagated error become unbounded in large networks of robots. In this paper, we propose a minimal but scalable sensor configuration for a fleet of vehicles localizing on the urban road. The cooperative localization is proven to be scalable if the sensors’ data are informative enough. The experimental results justify that pose uncertainty will remain at an acceptable level when the number of robots increases.

Teleoperation of On-Road Vehicles via Immersive Telepresence Using Off-the-shelf Components

The quality of visual information and response time are crucial aspects of any modern teleoperation system. This is especially true for operation of on-road vehicles, which must function in highly dynamic, unforgiving environments. In this work we demonstrate that a suitable teleoperation system can be exclusively composed of low-cost off-the-shelf components yet still meet the high performance demands of remotely driving a car on the road. The user is given immersive situational awareness through an on-board head-mounted display linked to an actuated stereoscopic camera, thereby maintaining depth perception and intuitive camera control. Communication speeds are evaluated over various wireless connection types, and a usability study shows that the system allows for advanced driving maneuvers while remotely controlled. 3G and 4G data networks are demonstrated to provide adequate bandwidth for the task given proper data compression, thus expanding the potential range for teleoperation. Applications for such a system are further discussed, extending to fleet management and autonomous vehicle safety measures.

Efficient L-shape fitting of laser scanner data for vehicle pose estimation

In this paper, we propose an efficient algorithm to fit a cluster of laser scan points with an L-shape. The algorithm partitions a cluster into two disjoint sets optimally in the sense of the least square error, and then fits them with two perpendicular lines. By exploiting the characteristics of both the laser scanner sensor and the fitting problem, the algorithm can test all the possible corner points while keeping the complexity as low as 9 times that of fitting a single pair of orthogonal lines, where the 9 times scaling factor is independent of the number of points in the cluster. Specifically, we exploit the property that the scanner data points are ordered either clockwise or counterclockwise, and incrementally construct the L-shape fitting problem rather than from scratch when the corner point is different. We extend our algorithm to provide multiple hypotheses on pose estimation, which are derived from L-shape fitting, to account for the ambiguity on the corner points. The extended algorithm only requires slightly more computation, which is tested and verified with real laser scanner data. The experimental results justify the correctness and efficacy of our algorithm.