Driver adaptive and context aware active safety systems using CAN-bus signals

Abstract

Increasing stress levels in drivers, along with their ability to multi task with infotainment systems cause the drivers to deviate their attention from the primary task of driving. With the rapid advancements in technology, along with the development of infotainment systems, much emphasis is being given to occupant safety. Modern vehicles are equipped with many sensors and ECUs (Embedded Control Units) and CAN-bus (Controller Area Network) plays a significant role in handling the entire communication between the sensors, ECUs and actuators. Most of the mechanical links are replaced by intelligent processing units (ECU) which take in signals from the sensors and provide measurements for proper functioning of engine and vehicle functionalities along with several active safety systems such as ABS (Anti-lock Brake System) and ESP (Electronic Stability program). Current active safety systems utilize the vehicle dynamics (using signals on CAN-bus) but are unaware of context and driver status, and do not adapt to the changing mental and physical conditions of the driver. The traditional engine and active safety systems use a very small time window (t<2sec) of the CAN-bus to operate. On the contrary, the implementation of driver adaptive and context aware systems require longer time windows and different methods for analysis. The long-term history and trends in the CAN-bus signals contain important information on driving patterns and driver characteristics. In this paper, a summary of systems that can be built on this type of analysis is presented. The CAN-bus signals are acquired and analyzed to recognize driving sub-tasks, maneuvers and routes. Driver inattention is assessed and an overall system which acquires, analyses and warns the driver in real-time while the driver is driving the car is presented showing that an optimal human-machine cooperative system can be designed to achieve improved overall safety.