Sequential experimentation to efficiently test automated vehicles

Automated vehicles have been under heavy developments in major auto and tech companies and are expected to release into market in the foreseeable future. However, the road safety of these vehicles remains a concern. One approach to evaluate their safety is via on-track experimentation, but this requires gigantic costs and time investments. This paper discusses a sequential learning approach based on kriging models to reduce the experimental runs and economize on-track experimentation. The approach relies on a heuristic simulation-based gradient descent procedure to search for the best next test scenario. We demonstrate our approach with some numerical test cases.

[1]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[2]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[3]  Barry L. Nelson,et al.  Stochastic kriging for simulation metamodeling , 2008, 2008 Winter Simulation Conference.

[4]  Huei Peng,et al.  Development and evaluation of collision warning/collision avoidance algorithms using an errable driver model , 2010 .

[5]  J. Kleijnen,et al.  Simulation Optimization Through Regression or Kriging Metamodels , 2017, High-Performance Simulation-Based Optimization.

[6]  Kangwon Lee,et al.  Longitudinal driver model and collision warning and avoidance algorithms based on human driving databases. , 2004 .

[7]  H. Kushner,et al.  Stochastic Approximation and Recursive Algorithms and Applications , 2003 .

[8]  Ding Zhao,et al.  Evaluation of automated vehicles in the frontal cut-in scenario — An enhanced approach using piecewise mixture models , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Jeremy Staum,et al.  Better simulation metamodeling: The why, what, and how of stochastic kriging , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[10]  Ding Zhao,et al.  Towards affordable on-track testing for autonomous vehicle — A Kriging-based statistical approach , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[11]  Uwe Kiencke,et al.  Automotive Control Systems , 2005 .

[12]  Evaluation of the Performance and Safety of Automated Vehicles ” for , 2013 .

[13]  Motoyuki Akamatsu,et al.  Automotive Technology and Human Factors Research: Past, Present, and Future , 2013 .

[14]  Jack P. C. Kleijnen,et al.  Kriging Metamodeling in Simulation: A Review , 2007, Eur. J. Oper. Res..

[15]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[16]  Jack P. C. Kleijnen,et al.  Application-driven sequential designs for simulation experiments: Kriging metamodelling , 2004, J. Oper. Res. Soc..

[17]  Ding Zhao,et al.  Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models , 2017, IEEE Transactions on Intelligent Transportation Systems.

[18]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[19]  M. Sasena,et al.  Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization , 2002 .