Heavy vehicle driver workload assessment: Task 3: Task analysis data collection

In various applications of land vehicle navigation and automatic guidance systems, Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning performance crucially depends on the attitude determination accuracy affected by gyro and accelerometer bias instabilities. Traditional bias estimation approaches based on the Kalman filter suffer from implementation complexity and require non-intuitive tuning procedures. In this paper we propose, as an alternative, a simple observer that estimates inertial sensor biases exclusively in terms of quantities with obvious geometrical meaning. By this, any multidimensional vector-matrix operations are avoided and observer tuning is substantially simplified. The observer has been successfully tested in a farming vehicle navigation system.

This paper describes a compact driver interface (CDI) for an in-vehicle navigation and route guidance system. The CDI consists of a partially flexible display device, a voice output subsystem, and a driver input device. The display device, which may be mounted in the dashboard or the headliner, is spatially removed from the input device, which is mounted in or near the armrest. Despite its small size, the CDI can comfortably accommodate the features required for route guidance and is easy to operate.

The author describes a modular approach to the design of an in-vehicle navigation and route guidance system that supports a phased implementation of the technology, and anticipates expected differences in implementation in different parts of the world and for different makes and models of vehicle. A series of sensors in the vehicle are used to determine the vehicle's position by dead reckoning and map-matching. The system then calculates the best route to the selected destination, taking into account the real-time traffic information received from a traffic management center, and presents route guidance instructions to the user as the route is traversed. Attention is given to modularity considerations, vehicle positioning, driver support, vehicle-to-infrastructure communications, and the role of standards. >

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers' route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

To compare patients' radiation exposure, technical feasibility, imaging quality and complication rate of percutaneous lung biopsies (PLBs) performed with a low-dose (LD) CT protocol under guidance of an optical navigation system. Fifty-two consecutive patients with suspected malignant lung lesions were enrolled and randomised into group 1 (PLBs under the guidance of the navigation system) and group 2 (PLBs under the guidance navigation system with an LD protocol). Patients' demographics, lesion features, procedure-related variables and CT image quality for group 2 were recorded and compared. Technical success was 100 % in both groups. The radiation dose to patients' chest was significantly lower in group 2 than in group 1 (group 1: mean TDLP 206 ± 59 mGy·cm, ~ 3.5 ± 1.0 mSv; group 2: 54.2 ± 46.2 mGy·cm, ~ 0.92 ± 0.78 mSv; P < 0.0001). The PNX rate was 12 % in group 1 and 11.1 % in group 2. The haemoptysis rate was 8.0 % in group 1 and 3.7 % in group 2. CT image quality obtained in group 2 was always rated as adequate and as excellent in 15 cases (56.0 %). An optical navigation system with LD CT protocol is useful for performing lung biopsies with decreased patient radiation exposure. • Navigation systems are useful tools in percutaneous imaging-guided procedures. • For lung biopsies, low-dose (LD) CT protocols may be used. • Combining LD protocols with optical CT navigation results in significantly reduced radiation exposure.

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

Connected vehicle (CV) technology aims to improve drivers’ situational awareness through audible and visual warnings displayed on a human–machine interface (HMI), thus reducing crashes caused by human error. This paper developed a driving simulator test bed to assess the readability and usefulness of the Wyoming CV applications. A total number of 26 professional drivers were recruited to participate in a driving-simulator study. Prior to driving the simulator, the participants were trained on both the concept of CV technology and the developed CV applications as well as the operation of the driving simulator. Three driving simulation scenarios were designed. For each scenario, participants drove two times: one with the HMI turned on and another one with the HMI turned off. After driving the simulator, a comprehensive revealed-preference survey was employed to collect the participants’ perceptions of CV technology and Wyoming CV applications. Results show that the Wyoming CV applications were most favored under poor-visibility driving conditions. Among the Wyoming CV applications, forward collision warning and rerouting applications were experienced as the most useful. Approximately 89% of the participants stated that the Wyoming CV applications provided them with improved road condition information and increased their experienced safety while driving; 65% of the participants stated the CV applications and the HMI did not introduce distraction from the primary task of driving. Finally, this paper concludes that the design of CV HMI needs to balance a trade-off between the readability of the warnings and drivers’ capability to safely recognize and timely respond to the received warnings.

The paper describes the design, implementation, and test of an autonomous vehicle navigation system using vehicle model and particle filter tracking algorithm. Typically, a vehicle navigation system comprises of real-time environment perception, vehicle localization, collision avoidance, path planning, and path following. In order to achieve the features for intelligent autonomous vehicle, a sensor suite of integrated inertial measurement unit (IMU), GNSS receiver, and incremental encoder is developed for vehicle position estimation. A map-aided path planning strategy is employed to generate a reference route. To this end, a UMI (User Machine Interface) is developed to facilitate the observation of a goal-oriented path tracking situation. The system utilizes particle filter algorithm to guide the vehicle following the planned path in terms of vehicle estimation control. The recursive particle filter is able to weight the cells and response the angle as well as estimated position information. All the sensors are integrated into an embedded computer platform and able to assess the autonomous driving capability. The test is conducted on campus by installing the sensor suite and embedded computer platform into an electricintegrated inertial measurement unit vehicle.

In our current research, we are developing a practical mobile vehicle navigation system which is capable of controlling multiple vehicles on the general environment. In order to realize the practical use, navigation system should have high accuracy and be low cost. Therefore, in this paper, we propose the vehicle navigation system which realizes vehicles navigation using high accuracy localization scheme by ceiling cameras with infrared filters and LED markers. Our key ideas are two topics. One topic is system integration which consists of high accuracy recognition system for LED marker and AGV controller with low cost. The other topic is novel AGV navigation scheme with high accuracy under occlusion condition. For practical use, mobile vehicles are required to continue its tasks with safety and high accuracy on temporary occlusion condition. Our developed system can navigate target vehicle based on estimation odometry error with simple algorithm. The performance of our proposed control system is experimented with our prototype.

This paper described an intelligent route guidance system that implements rule-based reasoning according to integrated information in onboard devices. A knowledge base of rules is built, and a control method is designed to overcome deficiencies of embedded systems, such as small available storage and limited computing speed. A prototype system is developed and the experimental evaluation shows that it makes reasoning effective in onboard devices. This work will improve the intelligence level of route guidance in vehicle location and navigation systems.

Route guidance in vehicular roadways has become an important and emerging method of congestion alleviation. The proliferation of low cost electronics such as sensors, wireless communication, and computing equipment has now made large scale vehicle navigation practical. The advanced computational equipment available has made performing complex algorithms in real time possible, the key to the operation of vehicle route guidance systems. While route guidance systems fundamentally strive for similar optimum operation, several important differences exist in the design of these systems. This paper presents an investigation of some of the main distinctions between these systems. Much work has been done in this field in the past, which is presented here as a classification and comparison of such systems

The 3rd International Conference on Vehicle Navigation and Information Systems

The most driving purpose is to traverse to the destination safely, efficiently, and comfortably. Two types of approaches could achieve the goals, including the static and dynamic approaches. In the static aspect, vehicles use the static road and traffic information to navigate. Conversely, in the dynamic aspect, vehicles adopt the dynamic information instead. However, both of the two approaches first require getting the vehicle’s location and then map the position on an e-map. Thus, this chapter first introduces some important vehicle location determination algorithms: the dead reckoning and global position system algorithms, in which the precision of location technologies are compared. Then, the map-matching algorithm is described in detail. Finally, various vehicle navigation approaches are detailed, in which the important topics include: the navigation architecture, the navigation routing algorithm, and navigation applications.