@conference {495, title = {CAN Coach: Vehicular Control through Human Cyber-Physical Systems}, booktitle = {Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems}, year = {2021}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

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). Using a cohort of six human subjects driving an instrumented vehicle, we compare the ability of the human-in-the-loop driver to achieve a constant time-gap control policy using only human-based visual perception to the car ahead, and by augmenting human perception with audible feedback from CAN sensor data. The addition of CAN-based feedback reduces the mean time-gap error by an average of 73\%, and also improves the consistency of the human by reducing the standard deviation of the time-gap error by 53\%. We remove human perception from the loop using a ghost mode in which the human-in-the-loop is coached to track a virtual vehicle on the road, rather than a physical one. The loss of visual perception of the vehicle ahead degrades the performance for most drivers, but by varying amounts. We show that human subjects can match the velocity of the lead vehicle ahead with and without CAN-based feedback, but velocity matching does not offer regulation of vehicle spacing. The viability of dynamic time-gap control is also demonstrated. We conclude that (1) it is possible to coach drivers to improve performance on driving tasks using CAN data, and (2) it is a true HCPS, since removing human perception from the control loop reduces performance at the given control objective.

}, keywords = {controller area network, cyber-physical systems, human-in-the-loop, vehicles}, isbn = {9781450383530}, doi = {10.1145/3450267.3450541}, url = {https://doi.org/10.1145/3450267.3450541}, author = {Nice, Matthew and Elmadani, Safwan and Bhadani, Rahul and Bunting, Matt and Sprinkle, Jonathan and Work, Dan} } @article {491, title = {Safer Adaptive Cruise Control for Traffic Wave Dampening}, year = {2020}, abstract = {

Our goal is to develop an adaptive cruise controller for vehicles at low speeds in stop-and-go traffic. Current adaptive cruise controllers can use radar sensors to follow a vehicle at high speeds (greater than 18 mph), but reach their limits if the lead vehicle{\textquoteright}s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car{\textquoteright}s speed. Some cruise controllers adapt to stop-and-go traffic, but these are mostly experimental and have yet to see widespread commercial implementation. These experimental models often have issues because of their limited data; consequently, the acceleration and deceleration can be jarring and uncomfortable to passengers. In contrast, because of our reliable sensor data, and the sensor configuration unique to the CAT Vehicle, our cruise controller will be capable of following cars at low speeds and functioning continuously, even when the car is stopped.

This project has the potential to interest automobile companies who could implement this technology in future automobiles. If our technology were to be implemented in future automobiles, it would make driving considerably more convenient for drivers. This technology could also potentially reduce the number of traffic accidents, as well as making drivers feel safer when navigating traffic. However, if errors were to occur, they could potentially put the car{\textquoteright}s passengers at risk, as well as the passengers in nearby vehicles.

Our project had a time frame of ten weeks during which we were able to model an adaptive cruise controller and test it in a simulation.

}, keywords = {Adaptive Cruise Control, stop-and-go, Vehicle Autonomy}, author = {Emily Baschab and Savannah Ball and Audrey Vazzana}, editor = {Jonathan Sprinkle} } @conference {450, title = {{A LiDAR Error Model for Cooperative Driving Simulations}}, booktitle = {IEEE Vehicular Network Conference}, year = {2018}, month = {12/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Taipei, Taiwan}, abstract = {

Cooperative driving and vehicular network simulations have done huge steps toward high realism.\ They have become essential tools for performance evaluation of any kind of vehicular networking application.\ Yet, cooperative vehicular applications will not be built on top of wireless networking alone, but rather fusing together different data sources including sensors like radars, LiDARs, or cameras.\ So far, these sensors have been assumed to be ideal, i.e., without any measurement error.\ This paper analyzes a set of estimated distance traces obtained with a LiDAR\ sensor and develops a stochastic error model that can be used in cooperative driving simulations.\ After implementing the model within the PLEXE\ simulation framework, we show the impact of the model on a set of cooperative driving control algorithms.

}, keywords = {autonomous vehicles, AV, CAV, LiDAR, self-driving cars, sensor, VNC}, author = {Michele Segata and Renato Lo Cigno and Rahul Bhadani and Matthew Bunting and Jonathan Sprinkle} }