Trustworthy Autonomous Vehicles Architecture (Internship) King Abdullah University of Science and Technology (KAUST)

Overview

Autonomous Vehicles (AVs) rely heavily on Artificial Intelligence (AI) and Machine Learning (ML) to navigate roads, recognize obstacles, and make driving decisions. However, a major issue with current AV architectures is that AI-based decision-making is often too slow or too complex for real-time safety-critical scenarios. 

Key Facts

Many AVs fail to react in time because their AI systems prioritize high-precision object recognition over immediate safety measures, leading to fatal crashes. Unfortunately, AV architectures from companies like Tesla, Cruise, Waymo, and Udacity have faced growing criticism due to increasing fatal incidents, largely attributed to failures in their AI/ML systems. 

In this Trustworthy Autonomous Vehicles Architecture (Internship) project from King Abdullah University of Science and Technology (KAUST) , we are studying the feasibility of current/new AI models for TPQD, the exploration and incorporation for sensing data (Radar, LIDAR, Cameras, etc.) to improve perception, improving the architecture details, define AI model for time estimation and trigger, and implement/evaluate the solution for simulation or integration with a robot car (which may need embedded background).