Tag Archives: feedback

Role Of Vision Processing With Artificial Neural Networks In Autonomous Driving

In next 10 years, the automotive industry will bring more change than we have seen in the last 50, due to technological advancement. One of the largest changes will be the move to autonomous vehicles, usually known as the self-driving car. Scientists from many universities are striving to implement vision processing with the artificial neural network to provide driver assistance in self-driving cars.

vision processing
Vision processing using convolutional artificial neural networks

Vision processing, as well as artificial neural networks, have been around for many years. Convolutional artificial neural networks (CNN) are sets of algorithms that extract meaningful information from sensor input. CNN’s are very computationally efficient at analyzing a scene. They are also able to identify objects as cars, people, animals, road signs, road junctions, road marking etc. enabling them to determine the relevant reality of the scene. As this system runs in real-time, the decision can be made as soon as the sensing part is complete.

One of the major steps in visual environment understanding for automotive applications is key points tracking and estimating ego-motion and environment structure subsequently from the trajectories of these key points. A propagation based tracking (PBT) method is popularly used to obtain the 2D trajectories from a sequence of images in a monocular camera setup.

The inputs from one or all of the sensors like LIDAR, RADAR, camera, IR, etc. are evaluated and decisions are taken accordingly. For example, if a car in the front suddenly brakes, the onboard computer would instantly verify the distance and calculate the speed with help of the existing sensors. Then it would apply the brakes faster than any human would be able to do. This method helps to prevent an accident with 90% efficiency.

The use of vision processing with CNN is rapidly increasing in automotive applications to enable camera-based autonomous driving. This technology sets a new driving standard. With this technology in our hand, fewer accidents, fewer fatalities, and less pollution are experienced. Vision processing in autonomous driving also enables efficient journeys, reduced crowding, car sharing, and packing cars in more tightly via vehicle to vehicle communication.

Control Loop Challenges of Wireless Systems

wifi_module

Automation is defined as using various control systems to operate equipment such that there is minimal human intervention. Closed control loops (feedback systems) regulate how other systems or devices behave by taking into consideration their output and making corrections based on feedback. An example of this feedback system is Progressive Automation Linear Actuators. In this article challenges of control loops in wireless systems are discussed.

Though embedded modules can be used be used with Wi-Fi, the aim of recent protocols is providing wireless networks with more focused support for control loops. For tight control loops, devices supporting the IEEE802.15.4 ZigBee standard can be used in supporting the recent protocols. Below is a Microchip’s MRF24WB Wi-Fi module.

For sharing a common medium of communication, sensors and controllers are required by a control system that is adopting wireless communication. The network’s quality is a trivial part of the functioning of the overall system when implementing a closed-loop control system using a common communication system.
Earlier short range wireless networks pose a problem since delay deadlines are not used in packet consideration and regardless of these requirements, the packets are treated the same. For a closed loop control system, this presents a major challenge since actuators influencing the system are controlled by the sensor’s data. Data delays lead to negative reinforcement, hence instead of a process being kept within close limits, it runs away. (more…)