Drowsy driving is a major threat caused by long distance driving, and this usually leads to road accidents and, in some cases, loss of life. But in the same vein, one can't really do without long-distance driving since it's part of a means to convey people, goods, and services. Interestingly, vehicles are made to function with adaptive cruise control these days so that drivers can reduce the amount of time they utilize the accelerator by placing the desired speed that could be for a vehicle ahead to imitate its speed. Still, once the vehicle in front moves away, your vehicle will return to its desired speed; even with navigation systems and cameras to coordinate the vehicle in tighter curves and barriers, it is still faced with some limitations. And so, in this write-up, I have considered utilizing artificial intelligence to improve autonomy and make it more robust. Integrating artificial intelligence will enable the system to analyze and adapt the driver's driving attitude, making it possible for the vehicle's speed to be regulated based on a similar pattern as the driver.
Artificial intelligence creates the opportunity to accurately achieve desired setting for autonomous driving to drivers' preference. At coordinated intervals, the camera and radar sensors collect driving information and relate it to a central processing unit housing the controller. The system unbundles the information to collect the data related to the driver's driving pattern; the artificial intelligence controller handles the control process of machine learning. But if, for any reason, details of unsafe driving forms part of the collected information, the system rejects such part. Meanwhile, the algorithm places more consideration on three areas of the driving pattern by drivers: distance to vehicles ahead, the force of acceleration, and fast response to changes. The processor is designed to have the capacity to differentiate so many driving patterns and yet specific enough to conveniently adapt to any driver's driving pattern.
Artificial Neural Network (ANN)
This consists of many neurons connected in a computational model and driven by a central nervous unit for machine learning and pattern matching. The neurons can be termed as the node, which can serve as output due to a corresponding input signal from the preceding node. Through this, data continues to move until it gives the final output. The weighting value for signal transmission is the link between every two nodes, which becomes the ANN's memory. Relatively, as adjustments in synaptic links are made in animals whenever they are learning, it is also when machines are learning; the weights of links between nodes in the ANN are also adjusted. This will then cause the output to vary. Other features that can also affect the output based on the network's connection are the activation function and weight value. Complex networks determine more network level of nodes with numerous input and output nodes. They can be used to better emphasize analysis on nonlinear problems. Still, a simple layered network like a network of three layers can be utilized in an ANN so that the first layer will contain the input nodes, which transmit signals through the synapses to the second layer. Then the signals are travels to the third layer, which is the output node. The learning process is achieved through a repetitive action executed in a layered ANN model utilizing a feedback method termed the backpropagation for weight adjustment. The fed input data is continuously repeated in the ANN for estimation leading to the ANN adapting. More so, a comparison of the actual output to the desired output after the estimation can create an error. This error is adapted in the ANN's weight so that in case of any reoccurrence, the output will have a minimal error.
Extreme Gradient Boosting (XGBoost)
This improves the shortcomings of the Gradient Boosting Decision Tree (GBDT). The XGBoost utilizes a boosting algorithm that depends on a regression tree that exhibits high running speed and good performance. A boosting algorithm is a set of learning algorithms that depend on the structure of being highly learnable and lowly learnable, as proposed by Kearns and Valiant. They emphasized that a situation is highly learnable if and only if it is lowly learnable. The learning stages start with creating and training the feeble models; as this continues for numerous models, the result is weighed by the boosting method to obtain the final result.
Specific strategy application is required for an environment with a multi-vehicular presence; this allows for a proper target selection. Adaptive Cruise Control engages an algorithm to determine which vehicle will be suitable for target selection, especially while in traffic. As the target on the same track has been identified, the information is sent to the longitudinal controller. But in a complex traffic situation, position, speed, confidence, and track positioning are considered, while other utilized criteria are sensors gathering information for data fusion and estimation and classic programming.
In conclusion, ACC integrated with artificial intelligence considerably increases safety, provides improved comfort, and optimum energy efficiency by gathering data based on the state of surrounding vehicles in real-time through sensors and using algorithms to carry out estimation to give the desired output based on perception.