SVTRIP

From a route to a speed profile: the end user defines a trip on a digital map (e.g. HERE maps) or a travel simulator (e.g. POLARIS), by providing origin, destination, and waypoints. SVTRIP then extracts a macroscopic definition of the trip with attributes such as speed limit, travel time, road class, and intersection type for each segment of the trip. In the last stage, SVTRIP sequentially generates a time-indexed speed signal that fits the attributes of each segment.

• Use cases:

  • Drive cycle generation. When developing and improving powertrains, automotive engineers rely heavily on drive cycles to (1) predict fuel consumption or electric range; (2) predict aging of the components (e.g., batteries or transmission); (3) appropriately size the components of the powertrains; and (4) predict or account for other variables. SVTRIP helps users create drive cycles that match their particular focus in a user-friendly way.

  • Data-augmentation for mesoscopic travel simulators: SVTRIP can be used to generate naturalistic drive cycles from the outputs of POLARIS [Add link] – POLARIS models outputs segment-by-segment travel times, but not detailed trajectories; this enables the linkage of POLARIS with Autonomie, which requires naturalistic drive cycles as an input.

  • Generation of speed horizon: a stochastic estimation of future speed can be used by control optimization algorithms to improve energy consumption or other metrics.

Data-driven. SVTRIP models how people drive by training on very large databases of recorded driving data. Every transition in speed that occurs in the output has been observed in the real-world dataset. We use a combination of Markov chains and machine learning to train on thousands of hours or real-world driving.

Naturalistic and Stochastic. The algorithms behind SVTRIP include the randomness inherently associated with human driving. As a result, the output of multiple runs/generations of SVTRIP with the same route target as an input results in a different result each time. The result is naturalistic, because it is made of speed transitions that actually occurred in the training dataset.

Route-specific. Each speed profile SVTRIP generates is particular to the route provided as input. For each segment of the route, the algorithm identifies the most relevant subset of data (e.g., high-speed highway driving) and generates a speed trace whose attributes match the target attributes of the segment (e.g., segment distance, initial speed, travel time, speed limit, stop or not at the end). Consequently, the user can generate drive cycles for a given route that has not been recorded in the training data, yet reproducing the naturalistic/stochastic nature of driving.

Development plan. SVTRIP is currently in development for public release. If you are interested in a demonstration or beta-testing, please contact Dominik Karbowski (Lead developer).