Team S.T.A.R.S.

Simulating Traffic Agents in Realistic Scenarios

Robotics Institute
Carnegie Mellon University

Team STARS
MRSD 2020 Project

Simulating Traffic Agents in Realistic Scenarios

In this project we will be building a simulation add-on to exhibit realistic traffic behavior and interactions for testing self-driving car algorithms. The cars inside the simulator will be controlled such that they exhibit realistic traits as displayed by human drivers on roads. It is estimated that for government regulators to give fully autonomous vehicles the green light, at least 6 billion miles of testing and information-gathering will be required. This can be expensive and can lead to fatalities. Inadequate testing is also very dangerous as they fail to account for the nuances of human driving. The unfortunate incident that happened in March’18 bears a cruel testament to this. According to reports the system design did not include a consideration for jaywalking pedestrians. Extensive prior testing could have prevented such occurrence.

S.T.A.R.S is an attempt to decouple testing and data collection, thereby allowing self-driving cars to learn from an abundant pool of real-world data at a fraction of the cost and more importantly without putting human lives at stake. To accomplish this, we will collect traffic data and build a model to learn realistic behavior as exhibited by human drivers from the data. The model will then be used to control cars inside a simulator.

In this project we want our scenarios to reflect what happens in the real world and will therefore be a blend of good and bad traffic practices that self-driving cars need to account for.
When we say ‘behavior’, it only refers to motion of a car as a response to other cars and traffic signs. We are not trying to model or simulate any other human traits.

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