Accident Alarming and Collision Avoiding System for Drowsy Drivers

Now a day’s road accident is one of the principal concerns in our states. Reckless automobile driving and driver drowsiness are the major motives in the back of those road accidents. The alarming rate of accidents and uncontrollable automobiles on the streetdemand an automatic system that could guide drivers right now in dangerous conditions. While any obstacle (just like the human, vehicle, and some other item) comes in front of the vehicle, control speed of the vehicle is the viable strategy to keep away from injuries. While a driver in a drowsy state or sleep state, give warning to the driver and control the velocity of the vehicle is the possible way to keep away from injuries. We endorse a solution in our mission to avoid avenue injuries and to manipulate the rate of automobiles. The machine will locate boundaries and motive force drowsiness using an ultrasonic Sensor and eye blink sensor and Arduino will execute a collision-avoidance gadget according to a pre-burnt code in the Arduino. The machine has also velocity manipulate features. It’s going to reduce or increase the rate of the vehicle relying on the impediment distance from the shifting car to decrease the damage or collision of an accident. Furthermore, if any obstacle from the bottom comes in the direction of cars, right now a buzzer will alert the driving force, and if any quick circuit happens in the engine element smoke sensor detecting and gives alert to the driving force and stop the automobile.

Development of a model of subjective feelings for the front visual quality during automobile driving

The main goal of this study is to develop a model of subjective feelings for frontal visibility, often called the feeling of ‘openness’, for automobile drivers. Moreover, this study proposed a method to overcome the limitations of the rating scale method, although it has been used in a great number of studies. To this end, eight potential design variables of automobiles were extracted from 25 candidate design variables. A questionnaire for evaluating the openness of an automobile was developed based on these variables. By using this questionnaire, a user experiment was conducted to investigate a relationship between the feeling of openness of automobiles and the design variables. Twenty-six participants evaluated the feeling of openness and the perceived rate of the design variables for 30 automobiles. The results showed that the variables such as the height of headlining, the height of cluster housing, the inclination angle of wind shield, and the volume of A-pillar were the critical design variables affecting the feeling of openness of the automobiles. Moreover, the optimal perceived level of each design variable was found out. The results of this study can be applied to designing automobile interior for good frontal visibility.

On-road driving behaviour characteristics of patients with brain injury

Background/aims To clarify the characteristics of automobile driving performance in patients with brain injury, 26 patients (patient group) and 26 driving instructors (control group) were evaluated via an on-road driving assessment. Methods On-road driving performance was evaluated using an event data recorder, driving analysis software, and a video-based driving assessment scale. The number of unsafe driving events, the score on the driving analysis software, and the score on the driving assessment scale were then compared between the two groups. Results The patient group had 12 unsafe events (7 braking, 4 acceleration, 1 curving), but the instructors only had two sudden braking events, both of which were to avoid a collision when another vehicle forcibly entered from outside the roadway. In the case of the driving analysis software, the patient group showed significantly lower scores for braking (P<0.05) and steering (P<0.05) than the control group. On the driving assessment scale, the patient group scored significantly lower for checking their blind spot during a lane change (P<0.01) and being aware of pedestrians at an intersection (P<0.01) than the control group. Conclusions These objective video and g-force-generated on-road driving assessments may provide important information regarding driving and advice for patients with brain injury.

AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models

Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply AI to identify individual behaviors (e.g., walking, eating, driving), analyze them, and offer personalized feedback to help individuals make improvements accordingly. While offering personalized feedback is more beneficial for drivers, most smart driver systems in the current market do not use it. This paper presents AutoCoach, an intelligent AI agent that classifies drivers’ into different driving-personality groups to offer personalized feedback. We have built a cloud-based Android application to collect, analyze and learn from a driver’s past driving data to provide personalized, constructive feedback accordingly. Our GUI interface provides real-time user feedback for both warnings and rewards for the driver. We have conducted an on-the-road pilot user study. We conducted a pilot study where drivers were asked to use different agent versions to compare personality-based feedback versus non-personality-based feedback. The study result proves our design’s feasibility and effectiveness in improving the user experience when using a personality-based driving agent, with 61% overall acceptance that it is more accurate than non-personality-based.

Unmanned parking method based on cloud platform

With the intelligent development of automobile driving, automatic parking technology is widely concerned by relevant technicians at home and abroad. Automatic parking technology is a method that can realize automatic parking in and out of parking Spaces without manual intervention. At present, automatic parking system has been equipped and used in many models, but there are many problems such as low parking accuracy and unable to effectively identify irregular parking Spaces. In order to solve the above problems, this article is based on a cloud platform developed a driverless car parking method, through the cloud server, GPS (Global Positioning System) and inertial navigation. It can according to the parking information and real-time Positioning planning adjustment path of the vehicle, and controls the complete vehicle posture adjustment, so as to improve the accuracy of parking and parking intelligent identification accuracy and ensure the safety and efficiency in the process of path planning. The effectiveness of the method is verified by simulation experiments with MATLAB software, which can be demonstrated by data.