Search Results

Research is under way to achieve autonomous driving (AD) on urban roads by 2020. This study focused on a right-turn situation at an intersection difficult to navigate even for human drivers. Many accidents have been reported when turning right due to poor visibility of traffic in an oncoming lane. An AD system is being developed that can recognize and avoid a potential risk of accident where another vehicle at an intersection causes poor visibility. The aim is to automate identification of vehicle locations and locations with poor visibility as well as decisions as to whether to decelerate, accelerate, or stop. As solutions, an obstacle detection frame (ODF) was created to identify the locations of other vehicles and a visibility detection frame (VDF) was created to identify the locations of blind spots. Detection frames are defined in an internal intersection map in the AD system.

This paper proposes a method of estimating road friction and tire slip angle in a cornering maneuver. The method can estimate front tire road friction accurately at low lateral acceleration because it is based on the tire self-aligning torque (SAT) that exhibits high sensitivity to road friction at low slip angles. Road friction and tire slip angle, which are mutually interdependent, are estimated simultaneously using an extended Kalman filter designed around a model describing the relationship between road friction and SAT and a vehicle lateral dynamic model. The front tire SAT is calculated with a mathematical model that describes the torque transmission characteristics from the electric power steering torque to SAT. Therefore, the proposed method is readily applicable to production vehicles. Results of an experimental study show that the change in road friction is instantaneously estimated at low lateral acceleration.