A Middle Way to Traffic Enlightenment

This paper introduces a novel approach that seeks a middle ground for traffic control in multi-lane congestion, where prevailing traffic speeds are too fast, and speed recommendations designed to dampen traffic waves are too slow. Advanced controllers that modify the speed of an automated car for wave-dampening, eco-driving, or other goals, typically are designed with forward collision safety in mind. Our approach goes further, by considering how dangerous it can be for a controller to drive so slowly relative to prevailing traffic that it creates a significant issue for safety and comfort. Our designed, implemented, and deployed algorithm is able to follow variable speed limits when others also follow it, avoid collisions with vehicles ahead, and adapt to prevailing traffic when other motorists are traveling well above the posted speeds.

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Enabling Mixed Autonomy Traffic Control

This research demonstrates a new capability of automated vehicles: mixed autonomy traffic control, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. This research introduces an extensible open-source hardware and software platform, and its implementation at a scale of 100 vehicles.

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So you think you can track?

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion).

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SAILing CAVs: Speed-Adaptive Infrastructure-Linked Connected and Automated Vehicles

This work demonstrates a new capability in roadway control: Speed-adaptive, infrastructure-linked connected and automated vehicles. We develop and deploy a lightly modified vehicle that is able to dynamically adjust the vehicle speed in response to posted variable speed limit messages generated by the infrastructure using LTE connectivity.

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Deploying traffic smoothing cruise controllers learned from trajectory data

Autonomous vehicle-based traffic smoothing con-trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway.

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CAN coach: Vehicular control through human cyber-physical systems

This work addresses whether a human-in-the-loop cyber-physical system (HCPS) can be effective in improving the longitudinal control of an individual vehicle in a traffic flow. We introduce the CAN Coach, which is a system that gives feedback to the human-in-the-loop using radar data (relative speed and position information to objects ahead) that is available on the controller area network (CAN). Conclusions include that it is possible to coach drivers to improve performance on driving tasks using CAN data.

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Online parameter estimation methods for adaptive cruise control systems

Two online methods are introduced to estimate model parameters of Adaptive Cruise Control enabled vehicles. The first technique is a recursive least squares approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering. All methods are tested both on synthetic data created using known model parameters, as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system.

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