A Complete Target Selection Method for ACC System Based on Statistics and Classification of Vehicle Trajectories

Abstract:

The uncertainty of driving behaviors of all cars and trajectories variation of preceding cars with changing path curvature make it hard for traditional radar-based Adaptive Cruise Control (ACC) system to choose its valid target, which is caused by the deficient judgment about the preceding curves and the behaviors of preceding cars. Through statistics and classification of the trajectories that host and preceding objects generate, the proposed method could differentiate the operating conditions of each car, either in straight lane, on curve or in lane-change, thus front path prediction and host vehicles future lane estimation can be well fulfilled. From radar and host cars information a coordinate that changes under several criteria can be established, based on which the trajectories of all cars can be classified and analyzed. This complete method can find the valid target for ACC system and enable the system to overcome some typical defects of traditional ACC, such as the confusion between lane-change and curve-enter of preceding cars, and also the speed of preceding cars can be modified as soon as they enter curves. HIL test have been conducted to validate the method.

• Periodical: Applied Mechanics and Materials (Volume 533)
• Pages: 316-320
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.533.316
• Online since: February 2014
• Authors: Jian Wu*, Shi Feng Geng, Yang Zhao
• Keywords: Adaptive Cruise Control, Hardware in the Loop, Host Vehicle, Mechatronic System, Preceding Vehicle, Radar-Based, Target Selection, Vehicle Trajectory

References

[1] Bishop, R.: Intelligent Vehicle Technology and Trends (ARTECH HOUSE ).

[2] Azim, Ed.: Handbook of Intelligent Vehicles (Springer 2012).

[3] Shunji, M.: SAE Technical Paper 2005-01-1478, (2005).

[4] Shunji, M.: SAE Technical Paper 2003-01-0013, (2003).

[5] Priscilla. E.: A Guide to Chi-squared Testing (John Wiley & Sons Inc, 1996).

[6] Philip, R.: Data Reduction and Error Analysis for the Physical Sciences (McGraw Hill Higher Education, 2002).