Naturalistic Vehicle Speed Profile Generation

Using high level route information, SVTRIP (Stochastic Vehicle TRIp Prediction) generates naturalistic vehicle speed profiles following training on a large datasets of recorded driving data. The workflow includes the following steps:

  1. End users define a trip on a digital map (e.g. HERE maps) or a travel simulator (e.g. POLARIS), by providing origin, destination, and waypoints
  2. 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
  3. SVTRIP sequentially generates a time-indexed speed signal that fits the attributes of each segment
Fig.1 - Route To Cycle

Product Highlights

  • 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. A combination of Markov chains and machine learning is used to train on thousands of hours or real-world driving.
  • Naturalistic and Stochastic. The algorithms 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).
Fig.2 - Target Trip Attributes

Application Examples

  • 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.
  • Data-augmentation for mesoscopic travel simulators: SVTRIP is used to generate naturalistic drive cycles from the outputs of POLARIS, enabling linkage with Autonomie as part of the SMART Mobility Workflow
  • Generation of speed horizon: a stochastic estimation of future speed can be used by control optimization algorithms to improve energy consumption or other metrics.


If you have questions or would like information on how to acquire SVTrip click the ‘Contact Us’ button.