Autonomous vehicles (AVs) have the potential to redefine the future of transportation. They promise to reduce human error, increase mobility for those unable to drive and optimize traffic management. But can we trust these systems to operate safely?
Networks, artificial intelligence (AI), edge computing, and embedded cameras and sensors all play critical roles in the operation of driverless vehicles. Equally important is the physical infrastructure of roadways and traffic signals and their supporting IT systems. For example, AVs may not function safely when poor road marking or heavy weather compromises camera and sensor visibility.
To improve real-time decision-making in driverless vehicles, cameras, radar, LiDAR and GPS work together with machine learning algorithms to navigate complex environments. These advanced technologies help vehicles identify hazards, interpret traffic signs, protect pedestrians and respond to changing situations.
However, the complex world of AVs also introduces risks, so it is important to train AI models to avoid errors or biases that could lead to accidents. Fortunately, AI-driven simulations allow for extensive testing before the deployment of AVs.
“Machine learning models improve over time, learning from diverse scenarios to enhance accuracy and reliability,” said Srinivas Gowda, Vice President of Autonomous Driving at International Motors. “These systems also employ anomaly detection to identify and address unusual events, such as erratic driving by other vehicles or unexpected obstacles.”
