  This paper focuses on the speed planning problem for connected and automated
vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic
signal timing for signalized intersections on the road is considered. The
eco-driving problem is formulated as a data-driven chance constrained robust
optimization problem. Effective red light duration (ERD) is defined as a random
variable, and describes the feasible passing time through the signalized
intersections. In practice, the true probability distribution for ERD is
usually unknown. Consequently, a data-driven approach is adopted to formulate
chance constraints based on empirical sample data. This incorporates robustness
into the eco-driving control problem with respect to uncertain signal timing.
Dynamic programming (DP) is employed to solve the optimization problem.
Simulation results demonstrate that the proposed method can generate optimal
speed reference trajectories with 40% less vehicle fuel consumption, while
maintaining the arrival time at a similar level when compared to a modified
intelligent driver model (IDM). The proposed control approach significantly
improves the controller robustness in the face of uncertain signal timing,
without requiring to know the distribution of the random variable a priori.
