Search Results

Abstract The Japanese population is aging rapidly, raising the number of traffic accidents involving elderly drivers. In Japan, single-vehicle accidents are a serious problem because they often result in fatalities. We analyzed these accidents by vehicle type, age group, and driving area. To examine the risk of accidents of the elderly drivers, their driving frequency needs to be considered, which is less. Moreover, it is difficult to know the actual distance driven by them. Therefore, in this paper, based on the assumption that the number of rear-end collisions is a proxy for the traffic volume, we used the number of such collisions as a control for the driving frequency. It was found that in single-vehicle accidents, elderly drivers were at higher risk than other age groups, especially when driving light motor vehicles (K-type vehicles) in non-urban areas.

Abstract The article considers the issues of a systemic approach to studying safety levels in transport operations and ways to increase the safety of the operator-machine system in Russian transport. The principal and problematic issues of reducing the risk of injury by preventing traffic accidents and reducing the severity of their impact have not been sufficiently addressed. When performing transport operations, there are often disagreements between the elements of the “Operator, Machine, and Environment” technological system due to the influence of external conditions and parameters of the constantly-changing environment in the workplace. This leads to a sharp increase in the number of failures of system elements, which reduces the level of safety of transport operations.

Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.

Abstract Driver drowsiness is the cause of many fatal accidents all over the world. Many research works have been conducted on detecting driver drowsiness for more than half a century, but statistical data show that such accidents have not decreased significantly. Most researchers have focused on using certain sensors and extracting their relevant features. However, there has been no research work on developing an algorithm to detect driver drowsiness independently from the input type. In this paper, a hybrid fuzzy-reinforcement learning drowsiness detection algorithm is presented. This algorithm is flexible to work with any number and any kind of data related to driver alertness. It estimates the level of alertness based on an arbitrary number of inputs. The algorithm extracts driving patterns specific to each driver and determines driver’s level of drowsiness using a continuous numerical variable rather than a discrete variable.

Abstract Potential collisions with oncoming traffic while turning left belong to the most safety-critical situations accounting for ~25% of all intersection crossing path crashes. A Left Turn Assist (LTA) was developed to reduce the number of crashes. Crucial for the effectiveness of the system is the design of the human-machine interface (HMI), i.e. defining how the system uses the calculated crash probability in the communication with the driver. A driving simulator study was conducted evaluating a warning strategy for two use cases: firstly, the driver comes to a stop before turning (STOP), and secondly, the driver moves on without stopping (MOVE). Forty drivers drove through three STOP and two MOVE scenarios. For the STOP scenarios, the study compared the effectiveness of an audio-visual warning with an additional brake intervention and a baseline. For the MOVE scenarios, the study analyzed the effectiveness of the audio-visual warning against a baseline.

Abstract In today’s competitive automobile market, driver comfort is at utmost importance and the bar is being raised continuously. Gear Shifting is a crucial customer touch point. Any issue or inconvenience caused while shifting gear can result into customer dissatisfaction and will impact the brand image. While there are continual efforts being taken by most of the car manufactures, “Double Bump” in gearshift has remained as a pain area and impact severely on the shift feel. This is more prominent in North-South (N-S) transmissions. In this paper ‘Double Bump’ is a focus area and a mathematical / analytical approach is demonstrated by analyzing ‘impacting parameters’ and establishing their co-relation with double bump. Additionally, the results are also verified with a simulation model.

Abstract Electric power steering (EPS) system is a kind of dynamic control system for vehicle steering, which can amplify the driver steering torque inputs to the vehicle to improve steering comfortable and performance, but the present EPS can’t cater to the driving habits of different people. In this paper, a personalized EPS controller is designed based on the driver behavior, which combines real-time driver behavior identification strategy with personalized assistance characteristic. Firstly, the driver behavior data acquisition system is designed and established, based on which, the input data of different kinds of drivers along with vehicle signals are collected under typical working conditions, then the identification of driver behavior online is realized using the BP neural network.