Traffic Situation Estimator for Adaptive Cruise Control (ACC)
| Title | Traffic Situation Estimator for Adaptive Cruise Control (ACC) |
|---|---|
| Summary | Traffic situation estimator using information from the sensors available in a smartphone |
| Keywords | |
| TimeFrame | |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Tony Larsson, Stefan Byttner and Cristofer Englund (Viktoria Swedisch ICT) |
| Level | Master |
| Status | Ongoing |
Vehicle driving can be made safer, more comfortable and energy efficient by help of speed adaptation to the traffic situation in a vehicles current neighborhood. The horizon for speed adaption can be increased by exchange of cooperative awareness messages (CAMs), in its turn enabling cooperative ACC (CACC). The CAMs to be exchanged are to a large extent based on GPS information fused with video and accelerometer data.
Propose and evaluate a method for how to gather traffic situation information observed in a vehicle’s local traffic neighborhood by using the GPS, video camera and accelerometer in a smartphone and use in this way gathered and analyzed information as guidance to adapt a vehicle’s cruising speed via an advice interface (for example based on voice or graphical messages).
Research Questions:
1) How implement such a method based on the use of 1 smartphone equipped with GPS, accelerometers and video camera (unless onboard cameras and GPS sensors can be accessed via CAN bus interface).
2) How tune the speed adaption advice (or control) to driver desired safety, comfort and energy saving demands.
A prototype need to be made using smartphones and thus some experience and/or strong interest in programming in Android or Apple IOS environment is required. Processing for analysis purposes may be done in MATLAB but an implementation must do the calculations in real-time.
Expected results are:
1) An implementation description in terms of architecture, algorithms and data structures proposed and used.
2) Definition of speed control behavior expressed as a set of goals.
3) Analysis of how well the prototype behaves relative to the goals.
References:
McCall J.C. and Trivedi M.M., "Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles," Proceedings of the IEEE , vol.95, no.2, pp.374-387, Feb. 2007.
Kohlhaas R., Schamm T., Lenk D., and Zollner J.M., "Towards driving autonomously: Autonomous cruise control in urban environments," IEEE Intelligent Vehicles Symposium Workshops 2013, pp.109-114, 23-23 June 2013.
Milanes V., Shladover S.E., Spring J., Nowakowski C., Kawazoe H., and Nakamura M., "Cooperative Adaptive Cruise Control in Real Traffic Situations," IEEE Transactions on Intelligent Transportation Systems, vol.PP, no.99, pp.1-10, 2013.
Key Words: Signal Processing, Driver Assistance, Automated Driving
Prerequisites: Image analysis, Control systems and Embedded programming
Time Frame: January 15 to June 1.
