# Title Team Members TA Documents Sponsor
Arnav Das
Darius Haery
Noah Salk
Dhruv Mathur design_document1.pdf
Noah Salk [noahs2], Arnav Das [arnavmd2], and Darius Haery [haery2]

Problem: Speed limits exist because authorities determined that abiding by these limits makes driving safer. The most common method of enforcing speed limits utilizes police officers with radar guns and is problematic for multiple reasons:

Police officers are greatly outnumbered by the number of drivers on the road. This results in drivers breaking the speed limits when no police car is in sight and slowing down when one is spotted. The low risk of getting caught is a chance drivers are willing to take in order to get where they're going faster.

Police officers are trained to handle dangerous situations and keep civility. Having them sit in a car for hours on end waiting for someone to speed is a great under utilization of their abilities.

There have been attempts by some cities to implement a speed camera system. Most, however, have been met with public disapproval for privacy concerns due to the camera. In addition, these cameras are typically placed on the side of the smaller roads and wouldn't be feasible for multi-lane highways. Signs that warn drivers of speed camera zone combined with their lack of prevalence results in the same issue as stationed police officers. When the speed camera zone ends, drivers begin to speed again.

Solution: A small packaged, speed measurement system can be embedded into the middle of a lane so that cars pass over it and placed frequently enough so that drivers need to always be aware of their speed in relation to the speed limits. If the measurement system determines the driver to be speeding (within a certain margin based on traffic, weather, and road conditions) a camera is angled so as to take a picture of the rear license plate. Computer vision software will be used to determine the license plate number without human interaction and the road, weather, and traffic conditions (the first two manually entered every day, the latter measured) will be noted in addition to the speed of the vehicle and sent as a bill to the driver.

The measurement system will consist of two object sensors (of type to be determined, could be sonar or some type of laser sensor) pointed upward and placed some distance from each other. The time between when the first sensor detects an object and the second sensor detects an object will be used to determine the vehicle speed. The device will wait until both sensors are clear before it resets and waits for another car to pass over. In winter months, a heater could be placed near the sensors and cameras to melt snow covering the device, although this could be unnecessary given that in general drivers use more caution when snow covers the roads.

A 3D printed prototype will hold all electronics, sensors, heaters, a micro-controller, and a camera. The device would be powered by the same power lines that service road lights (may need a AC-DC converter onboard) and therefore does not require a battery. If time permits, we could develop a communication method (wired or not) to interact with the device to extract speeding violations and transmit weather and road conditions.

Solution Components:
Speed Sensing - This will be accomplished using two line-break sensors pointed upward. The type of sensor has yet to be determined, as each type will provide different tolerances and complications. Potential sensor types include reflective laser sensors, sonar sensors, photoelectric sensor, etc. Calculating speed will be straight forward based on sensor separation distance and time between each sensor break. We need to make sure the sensor can operate at a high enough frequency and that our micro-controller can read input at a high enough frequency.

License Plate Sensing - This will be accomplished using a camera unit slightly aimed forward to take a picture of the vehicles license plate after it passes. The angle will be determined and the type of camera should be chosen based on price concerns mostly, but it should be able to take the picture before the vehicle gets too far away. OpenCV will be used to create a license plate number classifier from the snapped image and the program will be run on a micro-controller to be determined. Training will happen off-board prior to implementation on the micro-controller.

Powering the device - Local transportation authorities will be contacted to find out the type of power street lamps recieve (voltage levels, ac or dc, etc.) This information will be used to develop power electronics to power our device via the same lines that service street lamps. This will allow easy implementation into current road system.

Control unit - A micro-controller will be used to process all sensor and camera data in order to make decisions. This micro-controller should have a relatively fast operating frequency in order to make decisions before a speeding car passes over. It will need enough capabilities to classify a license plate via a neural network.

Criterion for success - If we can successfully measure the speed of a vehicle, determine whether it's speeding based on a number of variables, snap a picture of the license plate, and successfully determine the license plate number from the picture we will accomplished the goal of the project. The device needs to be verifiable, which means it needs to be semi-implemented on a road and tested to meet the criterion.

Electronic Automatic Transmission for Bicycle

Tianqi Liu, Ruijie Qi, Xingkai Zhou

Featured Project

Tianqi Liu(tliu51)

Ruijie Qi(rqi2)

Xingkai Zhou(xzhou40)

Sometimes bikers might not which gear is the optimal one to select. Bicycle changes gears by pulling or releasing a steel cable mechanically. We could potentially automate gear changing by hooking up a servo motor to the gear cable. We could calculate the optimal gear under current condition by using several sensors: two hall effect sensors, one sensing cadence from the paddle and the other one sensing the overall speed from the wheel, we could also use pressure sensors on the paddle to determine how hard the biker is paddling. With these sensors, it would be sufficient enough for use detect different terrains since the biker tend to go slower and pedal slower for uphill or go faster and pedal faster for downhill. With all these information from the sensors, we could definitely find out the optimal gear electronically. We plan to take care of the shifting of rear derailleur, if we have more time we may consider modifying the front as well.

Besides shifting automatically, we plan to add a manual mode to our project as well. With manual mode activated, the rider could override the automatic system and select the gear on its own.

We found out another group did electronic bicycle shifting in Spring 2016, but they didn't have a automatic function and didn't have the sensor set-up like ours. Commercially, both SRAM and SHIMANO have electronic shifting products, but these products integrate the servo motor inside the derailleurs, and they have a price tag over $1000. Only professionals or rich enthusiasts can have a hand on them. As our system could potentially serve as an add-on device to all bicycles with gears, it would be much cheaper.

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