Applications of AI in Optimizing Autonomous Vehicles

AI is an integral tool in autonomous vehicles and can be applied in various areas to make autonomous vehicles safe, effective, and cost-efficient.

FREMONT, CA: Many industries are already using autonomous vehicles in certain fields, such as the military, agriculture, and transportation. Many feature of autonomous vehicles are possible due to artificial intelligence (AI), machine learning (ML), and sensor technology to collect data, plan and execute trajectory. AI technologies seek to replicate human cognitive and motor skills that can eliminate inconsistencies and make autonomous safe and reliable for regular consumers.

Streamlining AI technology in autonomous vehicles makes them reliable. AI can be applied in the following dimensions:

Sensor data processing: The central computer of the vehicle stores data from sensors. Sensors fulfill the function of human vision. Sensors provide vehicles data about the road, other vehicles, and features of the road. Sensors have the ability to be better than human perception when algorithms can make sense of data generated in real time. Artificial neural networks (ANN) detects and identifies objects around the vehicle. Multiple sensors should have dedicated hardware/software modules for each sensor. This system allows for parallel processing of data and faster decision making. Each sensor unit can use different AI algorithm to communicate to the central processing computer.

Path planning: Path planning helps in traffic management. It plans the optimal trajectory of the vehicle. AI detects the best routes to avoid delays and congestion. ML uses previous driving experiences to take the best route in the future.

Path execution: Path execution needs to be intuitive. Autonomous vehicles fail in real life situations of adverse weather because conditions in simulations are different to naturally occurring weather events. AI technology react to new data unpredictably.

Observing vehicle condition: Predictive maintenance diagnose vehicle in advance to evade loses in the future. It saves time and money. Algorithms can use on-board and off-board data in predictive maintenance. Machine learning algorithms used are classification algorithms such as logistic regression, support vector machines, and random forest algorithm.

Insurance data collection: AI collects data of the vehicles performance and its surrounding features during accidents for insurance companies when processing claims. Since autonomous vehicles are self-driven, liability shifts from drivers to manufacturers. 

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