With a goal to improve transportation safety, this paper proposes a collaborative driving framework based on assessments of both internal and external risks involved in vehicle driving. The internal risk analysis includes driver drowsiness detection and driver intention recognition that helps to understand the human driver's behavior. Steering wheel data and facial expression are used to detect the driver's drowsiness. Hidden Markov models are adapted to recognize the driver's intention using the vehicle's lane position, control, and state data. For the external risk analysis, a co-pilot utilizes a collision avoidance system to estimate the collision probability between the ego vehicle and other nearby vehicles. Based on the risk analyses, we design a novel collaborative driving scheme by fusing the control inputs from the human driver and the co-pilot to obtain the final control input for the ego vehicle under different circumstances. The proposed collaborative driving framework is validated in an assisted-driving testbed, which enables both autonomous and manual driving capabilities.

This paper proposes a driver assistance system with a collaborative control framework between the human driver and a Collision Avoidance System (CAS) for vehicles on the road while considering the driver drowsiness status. Driver drowsiness detection is performed with two inputs: driver's facial data from a camera mounted in front of the driver and steering wheel data from the car controller system. We use the driver's drowsiness state as an input to the collaborative control framework in which the CAS algorithm runs in parallel with the human control and only intervenes under certain situations to assist the human driver. Experiments were performed on a simulated vehicle driving testbed to evaluate our drowsiness detection system and demonstrate the effectiveness of the proposed collaborative control framework.

Vegetation coverage is an important indicator for forecasting geological disasters in mountainous areas such as landslide. However, it is a challenge to extract vegetation coverage in complex terrain from SAR image. A major problem is that the variation of the backscatter coefficient of the same object varies with the local incidence angle. As a result, a large number of discrete points appear in the classification results. Focus on this problem, a region-based method of vegetation coverage monitoring has been presented in this paper. Based on the results of yamaguchi decomposition and SVM algorithm, the Watershed algorithm is used to over-segment the image and regions are merged on a pixel-voting basis. When the modified method was applied on RADARSAT-2 data, the research results show that the completeness and correctness are improved compared with the method based on yamaguchi decomposition and svm algorithm.

Map building is a fundamental task in many robotic applications. In this paper, we propose a novel approach for 3D mapping of indoor environments which allows a robot avatar to collaborate with a human seamlessly through a virtual reality (VR) device. The 3D map is created using the 3D data from an RGB-D camera mounted on the robot and simultaneously transmitted to a remote server, and then rendered to the VR device. On the other hand, the intentions of the user are inferred using the motion of the head movement based on hidden Markov models (HMMs), and then interpreted into commands to control the robot. We implement the proposed approach based on a modified Pioneer robot platform. The experimental results show the feasibility of the proposed system.

In this correspondence paper, we develop a teleoperated robotic 3-D mapping (TeRoM) system which enables efficient human-guided mapping of remote environments for realistic rendering and visualization. First, the hardware design of the TeRoM system is proposed which is based on a Pioneer mobile robot platform and a rotating RGB-D camera. A client/server architecture is developed to allow the data to be processed in a remote server, which makes it possible to implement 3-D mapping on robots with limited resources. Second, a 3-D map is created in real-time while an operator controls the robot and the pan-tilt unit remotely using a joystick. Then the map is converted into a mesh using the marching cubes algorithm and optimized to reduce the data volume. Finally the mesh is imported and rendered in a 3-D rendering engine for interactive and intuitive display. We evaluate the performance of the TeRoM system in terms of accuracy, processing speed, reliability, and manipulability.

Semantic information can help both humans and robots to understand their environments better. In order to obtain semantic information efficiently and link it to a metric map, we present a semantic mapping approach through human activity recognition in an indoor human-robot coexisting environment. An intelligent mobile robot platform can create a 2D metric map, while human activity can be recognized using motion data from wearable motion sensors mounted on a human subject. Combined with pre-learned models of activity-to-furniture type association and robot pose estimates, the robot can determine the distribution of the furniture types on the 2D metric map. Simulations and real world experiments demonstrate that the proposed method is able to create a reliable metric map with accurate semantic information.

In this paper, we propose and develop a small-scale research platform for intelligent transportation systems (ITSs). Our platform has four main parts, i.e., an arena, an indoor localization system, automated radio-controlled (RC) cars, and roadside monitoring facilities. First, to mimic traffic environments, we build an arena with a wooden floor, mock buildings, and streets. Second, to facilitate feedback control for trajectory following, an indoor localization system is set up to track the RC cars. Third, both autonomous driving RC cars and human driving RC cars are developed, based on an automated RC car design. The automated RC cars can receive control signals from a computer through an Xbee RF module and control the front and rear wheels through motors. A new control algorithm is developed to allow the RC cars to track predefined trajectories. Finally, we implement an example of roadside monitoring, which uses a fish-eye camera associated with advanced video processing for image segmentation, object identification, and tracking. Experiments are performed to demonstrate the effectiveness of the designed platform. We also discuss possible ITS research problems that can be studied in this testbed.