Collision avoidance system

Abstract: Herein is presented a positioning, navigation and collision avoidance system for ships, aircraft, land vehicles and the like, which utilizes a geo-referenced digital orthophotograph data-base and a positioning signal to display upon a computer stereo graphics device a high visibility dynamic photographic image of the user's immediate environment, including both moving and stationary obstacles. The position and temporal data along with the geo-referenced elevation data utilized to derive the digital orthophotograph(s) can serve to warn the user of nearby obstacles; and optionally, to implement semi-automatic avoidance. Substituting user generated x-y-z positions and times, the system may be used in a static mode as a flight simulator or a simulator for other modes of transportation. The system may also be used as a mobile Geographic Information Systems decision making tool with the addition of user supplied geo-referenced digital data layers.

Abstract: This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

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.

Abstract: Various examples are described for an artificial intelligence valet system. In one example, among others, a distributed information sharing system can obtain route information associated with a geographic area and provide the route information to a user vehicle in response to a request. In another example, an autonomous user vehicle can receive a request to autonomously proceed to a user defined location; obtain route information; and determine a route to the user defined location using the route information. In another example, a collision avoidance system can determine if an object is in a path of travel of a vehicle based at least in part upon sensory data and maneuver the vehicle based at least in part upon an object determination. In another example, an accident reporting system can determine an occurrence of a violation of a vehicle; obtain recordings of the environment surrounding the vehicle; and report the violation.