Using Glance Behaviour to Evaluate ACC Driver Controls in a Driving Simulator
This paper examines the glance behaviour of drivers while interacting with two different driver-vehicle interface concepts for an Adaptive Cruise Control (ACC) system. With the integrated concept, the speed and following distance controls were located on the steering wheel whereas with the divided concept the speed control was moved to the dashboard. A virtual Head-Up Display (HUD) was used to show the ACC settings and current speed. Twelve subjects (19 to 53 years old) drove a rural road course in a fixed-base driving simulator while being verbally instructed to adjust the speed and/or following distance of the ACC system. Dividing the controls between the steering wheel and dashboard caused significantly larger mean and maximum glance times and a lower glance frequency to the displays and controls. The percent glance time “off-road” furthermore increased significantly during task completion. Other significant results were observed between the task type and task length.
Driver Inattention: A Contributing Factor to Crashes and Near-Crashes
Driver distraction, or inattention, has been receiving wide media attention recently as many state legislatures are considering various levels of restricting cell phone use. Research has been conducted using a variety of experimental methods to determine the level of risk associated with driving inattention. While most of this research suggests that inattention impairs driving, there have been no studies to directly link driving inattention to crashes. Data from the 100-Car Naturalistic Driving Study, an instrumented vehicle study for which data was collected on 100 drivers in the Washington, DC metropolitan area for 12 months, were used in the following analyses. Crashes and near-crashes were identified in the data using post-hoc triggers based upon driving performance metrics, (i.e. hard braking). Results suggest that inattention contributed to 78% of all crashes collected over the 12 month data collection period.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.