02431nas a2200229 4500008004100000020001800041245007000059210006800129260005900197520169800256653002801954653002701982653002202009653001302031100001802044700002102062700001902083700001802102700002302120700001402143856004402157 2021 eng d a978145038353000aCAN Coach: Vehicular Control through Human Cyber-Physical Systems0 aCAN Coach Vehicular Control through Human CyberPhysical Systems aNew York, NY, USAbAssociation for Computing Machinery3 a

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.

10acontroller area network10acyber-physical systems10ahuman-in-the-loop10avehicles1 aNice, Matthew1 aElmadani, Safwan1 aBhadani, Rahul1 aBunting, Matt1 aSprinkle, Jonathan1 aWork, Dan uhttps://doi.org/10.1145/3450267.345054102204nas a2200169 4500008004100000245006100041210006100102520163400163653002801797653001601825653002101841100001901862700001901881700002001900700002301920856009101943 2020 eng d00aSafer Adaptive Cruise Control for Traffic Wave Dampening0 aSafer Adaptive Cruise Control for Traffic Wave Dampening3 a

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’s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car’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’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.

10aAdaptive Cruise Control10astop-and-go10aVehicle Autonomy1 aBaschab, Emily1 aBall, Savannah1 aVazzana, Audrey1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/safer-adaptive-cruise-control-traffic-wave-dampening-001584nas a2200241 4500008004100000245006200041210005800103260003400161520086600195653002401061653000701085653000801092653001001100653002201110653001101132653000801143100002001151700002201171700001901193700002101212700002301233856008601256 2018 eng d00a{A LiDAR Error Model for Cooperative Driving Simulations}0 aLiDAR Error Model for Cooperative Driving Simulations aTaipei, TaiwanbIEEEc12/20183 a

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.

10aautonomous vehicles10aAV10aCAV10aLiDAR10aself-driving cars10asensor10aVNC1 aSegata, Michele1 aCigno, Renato, Lo1 aBhadani, Rahul1 aBunting, Matthew1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/lidar-error-model-cooperative-driving-simulations