THANK YOU FOR SUBSCRIBING
How AI Applications have created an impact on transportation
FREMONT, CA: Artificial intelligence is changing the transport sector. From helping cars, trains, ships and aeroplanes to function autonomously, to making traffic flows smoother, it is already applied in numerous transport fields. Artificial intelligence-led autonomous transport could for instance help to reduce the human errors that are involved in many traffic accidents.
Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.
Self-driving Vehicles:
A self-driving car is a vehicle capable of sensing its environment and operating without human involvement. A human passenger is not required to take control of the vehicle at any time, nor is a human passenger required to be present in the vehicle at all. An autonomous car can go anywhere a traditional car goes and do everything that an experienced human driver does. These cars rely on sensors, actuators, complex algorithms, machine learning systems, and powerful processors to execute software.
They create and maintain a map of their surroundings based on a variety of sensors situated in different parts of the vehicle. Radar sensors monitor the position of nearby vehicles. Video cameras detect traffic lights, read road signs, track other vehicles, and look for pedestrians. Lidar (light detection and ranging) sensors bounce pulses of light off the car’s surroundings to measure distances, detect road edges, and identify lane markings. Ultrasonic sensors in the wheels detect curbs and other vehicles when parking.
Sophisticated software then processes all this sensory input, plots a path, and sends instructions to the car’s actuators, which control acceleration, braking, and steering. Hard-coded rules, obstacle avoidance algorithms, predictive modelling, and object recognition help the software follow traffic rules and navigate obstacles.
Traffic Detection (and Traffic Signs):
Traffic sign detection and recognition play an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. First, the images of the road scene are converted to grayscale images, and then the grayscale images are filtered with simplified Gabor wavelets (SGW), where the parameters were optimised. The edges of the traffic signs are strengthened, which is helpful for the next stage of the process. Second, the region of interest is extracted using the maximally stable extremal regions algorithm and classified as the superclass of traffic signs using the support vector machine (SVM). Finally, convolution neural networks are used with input by simplified Gabor feature maps, where the parameters are the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.
Automated Licence Plate Recognition
Automated licence plate readers (ALPRs) are high-speed, computer-controlled camera systems that are typically mounted on street poles, streetlights, highway overpasses, mobile trailers, or attached to police squad cars. ALPRs automatically capture all licence plate numbers that come into view, along with the location, date, and time. The data, which includes photographs of the vehicle and sometimes its driver and passengers, is then uploaded to a central server. Vendors say that the information collected can be used by police to find out where a plate has been in the past, to determine whether a vehicle was at the scene of a crime, to identify travel patterns, and even to discover vehicles that may be associated with each other. Law enforcement agencies can choose to share their information with thousands of other agencies.
Stationary ALPR cameras are installed in a fixed location, such as a traffic light, a telephone pole, the entrance of a facility, or a freeway exit ramp. These cameras generally capture only vehicles in motion that pass within view. If multiple stationary ALPR cameras are installed along a single thoroughfare, the data can reveal what direction and what speed a car is travelling. If the data are stored over time, they can reveal every time a particular plate has passed a given location, allowing the government to infer that the driver likely lives or works close by.
More in News