The National Highway Traffic Safety Administration (NHTSA) estimates that about 9 per cent of all motor vehicle accidents occur due to some kind of lane changing or merging collision. These types of crashes are often dangerous because the other vehicle involved in the accident is caught off-guard and left in a very vulnerable position. Thus, changing lanes abruptly leaves the accident victim unable to react to avoid the crash.
The main cause of this is human error in recognition and decision making. Active safety systems have thus great potential for increasing vehicle safety at turns and intersections. It can issue warnings to the driver to take control of the vehicle in critical situations.
Some premium cars have already implemented some features for the vehicle collision avoidance system, but it's not yet scaled to every segment. Collision avoidance systems will act as a great boon to mankind in solving problems of navigation, road accidents and will help in better traffic management.
In the proposed system, wheel speed encoders, accelerometers and gyroscopes will be used as sensors for collision avoidance. These sensors are placed on all four corners of the vehicle, and the data will be acquired by a custom data acquisition system implemented on the FPGA. Sensor noise filtering and basic sensor data fusion will be performed using the ARM CORTEX 9 Processor provided on the FPGA. This information is then transferred to the cloud using the Azure cloud connectivity board.
The trajectory of all the vehicles in a window of interest around each vehicle will be estimated by a regression-based machine learning algorithm and if the estimated trajectories point to a possible collision, the driver will be alerted with visual and audio warnings. On the cloud, a learning algorithm will be developed which will be trained using predefined datasets to send the necessary warning signals to the vehicle thereby alerting the driver about the forthcoming collision.
This information can be extrapolated and integrated into a traffic management system in which the trajectories can be used to prevent congestion of vehicles at traffic intersections by advising the drivers of the speed with which they need to drive while approaching the intersection.

Project Proposal

1. High-level project introduction and performance expectation

In this proposed system, we use three types of sensors, accelerometer, gyroscopes and wheel speed encoders. Wheel speed encoders are attached to the four corners of the vehicle, to capture the speed of the wheel. Accelerometers and gyroscopes are also attached to the 4 wheels of the car to receive the respective acceleration or deceleration and angular velocity. All these components are connected to the FPGA board using the GPIO pins available on the Board itself. The board is equipped with ARM Cortex 9 processor which consists of the Linux OS. 

The FPGA then communicates with the Cloud using Azure Cloud Connectivity board. The cloud algorithm will receive such information from all the vehicles on the road, computing the number of vehicles in the vicinity of the driver. The data gathered helps in determining the trajectory of the vehicle. Any change in the trajectory of the other vehicles, the driver will be notified through audio and video. This helps in avoiding collisions by alerting the driver about the advancing vehicles. The required information is sent back to the FPGA board and the driver is informed about the same.

The purpose of this project is to reduce the probability of getting into an accident and improve the traffic flow. Traffic congestion is also avoided and the driver will be notified to use alternative routes. Driver is alerted well in advance if any car has changed its trajectory and collisions will be avoided.

2. Block Diagram

3. Expected sustainability results, projected resource savings

This project design should be able to use the sensor data acquired by a custom data acquisition system implemented on the FPGA. Sensor noise filtering and basic sensor data fusion will be performed using the ARM CORTEX 9 Processor provided on the FPGA. If the driver fails to spot any vehicles in the vicinity of the vehicle, the driver will be alerted by the behavioural control module installed on the FPGA and also with the audio/video output on the vehicle. The trajectories of the vehicles will be monitored by the cloud and the driver will get an update if there’s a vehicle overtaking from either side of the vehicle.

Applications

• Useful in narrow lanes and hilly regions where lanes are curved.
• Prevents minor collisions and thereby reduces scratches and dents.
• Accidents due to blind spots can be prevented
• Smoother traffic flow and saves time

4. Design Introduction

5. Functional description and implementation

6. Performance metrics, performance to expectation

7. Sustainability results, resource savings achieved

8. Conclusion