  In this paper we present the architecture of the Kyber-E2E submission to the
map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023,
which achieved first place. We employed a modular architecture for our solution
consists of five main components: sensing, localization, perception,
tracking/prediction, and planning/control. Our solution leverages
state-of-the-art language-assisted perception models to help our planner
perform more reliably in highly challenging traffic scenarios. We use
open-source driving datasets in conjunction with Inverse Reinforcement Learning
(IRL) to enhance the performance of our motion planner. We provide insight into
our design choices and trade-offs made to achieve this solution. We also
explore the impact of each component in the overall performance of our
solution, with the intent of providing a guideline where allocation of
resources can have the greatest impact.
