Collision avoidance system

About: Collision avoidance system is a research topic. Over the lifetime, 1788 publications have been published within this topic receiving 23667 citations.

Automated road transportation system

Abstract: An automated transportation system for vehicular travel along a roadway is presented. The roadway can be any hard surface that contains an embedded metallic guiderail along its center. A proximity transducer (metal detector) installed under the vehicle, senses the metallic guiderail and sends steering commands to the vehicle's steering actuators to keep the vehicle moving in the center of the roadway directly over the guiderail. Transponders are embedded along the guiderail at selected points for automatic roadway identification, position determination and automatic speed, headway and route selection and automatic traffic control. The system also provides automatic branching and lane changing. The vehicle's control system includes sensors, signal processors, a microprocessor and various power actuators that are connected to the vehicle's steering, braking and accelerator systems. An on-board collision avoidance system using sonic or radar detection means is also provided. The vehicle can be programmed to automatically follow a pre-selected route by inserting route instructions into the microprocessor's memory where it can be retrieved and used any number of times. Magnetometer sensors are also embedded along the roadway to detect the presence and speed of all vehicles, thereby allowing ordinary, manually-controlled vehicles to use the same roadways as the automatically controlled vehicles for a mixed traffic flow. The guiderails and transponders can be embedded in essentially all existing streets and highways to provide an economical comprehensive fully automated road transportation system for private and commercial vehicles.

A comprehensive examination of naturalistic lane-changes

Abstract: This research effort provided valuable insight into the nature and severity of lane changes in a naturalistic driving environment. Sixteen commuters who normally drove more than 25 miles (40 km) in each direction participated. The two research vehicles were a sedan and a sport utility vehicle; each participant drove each vehicle for ten days. Data gathering was automatic, and no experimenter was present in the vehicle. There were 8,667 lane changes observed over 23,949 miles of driving, making this the largest known data collection effort for the study of lane changes. Analysis of the full data set resulted in many interesting findings regarding the frequency, duration, urgency, and severity of lane changes in regard to maneuver type, direction, and other classification variables. A subset of the full data set (500 lane changes) was then analyzed in greater depth using the sensor data collected by the instrumented vehicle. The sampled lane changes were generally of the more severe and urgent types since these are the cases in which a lane change collision avoidance system (CAS) is likely to be of greatest help. Variables analyzed for the sampled lane changes included turn signal use, braking behavior, steering behavior, eye glance patterns, and forward and rearward area analysis. The concept of a safety envelope for lane changes was then developed using the forward and rearward area analyses. Finally, the data were used to provide recommendations for designers of a lane change CAS in terms of display location and activation criteria. Overall, the research described in this report provides insight into the behaviors and parameters associated with lane changes, while the naturalistic data archive has the potential to address other questions related to driving behavior.

Development of a collision avoidance system

Abstract: The analysis of a rear-end collision warning/ avoidance (CW/CA) system algorithm will be presented. The system is designed to meet several criteria: 1. System warnings should result in a minimum load on driver attention. 2. Automatic control of the brakes should not interfere with normal driving operation.

Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set

Abstract: The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. In particular, improving safety at intersections has been identified as a high priority due to the large number of intersection-related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections and validating them on real traffic data. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on (1) support vector machines and (2) hidden Markov models, which are two very popular machine learning approaches that have been used successfully for classification in multiple disciplines. However, existing work has not explored the benefits of applying these techniques to the problem of driver behavior classification at intersections. The developed algorithms are successfully validated using naturalistic intersection data collected in Christiansburg, VA, through the U.S. Department of Transportation Cooperative Intersection Collision Avoidance System for Violations initiative. Their performances are also compared with those of three traditional methods, and the results show significant improvements with the new algorithms.

Satellite based collision avoidance system

Abstract: This invention provides a method and apparatus to provide coordinated evasive maneuver commands to aircraft to avoid collisions. More specifically, the invention comprises a GPS system to determined the location of aircraft, control logic to calculate evasive maneuvers, display aircraft tracking information, coordinate the evasive maneuver with the intruding aircraft, and give a synthetic voice warning and command to the pilots.