Speed trajectory data from adaptive eco-driving applications

The eco-approach and departure (EAD) application for signalized
intersections has been proved to be environmentally efficient in a
Connected and Automated Vehicles (CAVs) system. In the real-world traffic,
the traffic-related information received from sensing or communication
devices is highly uncertain due to the limited sensing range and varying
driving behaviors of other vehicles. This uncertainty increases the
difficulty to predict the actual queue length of the downstream
intersection. It further brings great challenge to derive an energy
efficient speed profile for vehicles to follow. This research
proposes an adaptive strategy for connected eco-driving towards a
signalized intersection under real world conditions including uncertain
traffic condition. A graph-based model is created with nodes representing
dynamic states of the host vehicle (distance to intersection and current
speed) and indicator of queue status and directed edges with weight
representing expected energy consumption between two connected states.
Then a dynamic programing approach is applied to identify the optimal
speed for each vehicle-queue-signal state iteratively from downstream to
the upstream. The uncertainty can be addressed by formulating stochastic
models when describing the transition of queue-signal state. For uncertain
traffic conditions, numerical simulation results show an average energy
saving of 9%. It also indicates that energy consumption of a vehicle
equipped with adaptive EAD strategy and a 100m-range sensor is equivalent
to a vehicle with conventional EAD strategy and a 190m-range sensor. To
some extent, the proposed strategy could double the effective detection
range in eco-driving.