  Autonomous vehicles are conceived to provide safe and secure services by
validating the safety standards as indicated by SOTIF-ISO/PAS-21448 (Safety of
the intended functionality). Keeping in this context, the perception of the
environment plays an instrumental role in conjunction with localization,
planning and control modules. As a pivotal algorithm in the perception stack,
object detection provides extensive insights into the autonomous vehicle's
surroundings. Camera and Lidar are extensively utilized for object detection
among different sensor modalities, but these exteroceptive sensors have
limitations in resolution and adverse weather conditions. In this work,
radar-based object detection is explored provides a counterpart sensor modality
to be deployed and used in adverse weather conditions. The radar gives complex
data; for this purpose, a channel boosting feature ensemble method with
transformer encoder-decoder network is proposed. The object detection task
using radar is formulated as a set prediction problem and evaluated on the
publicly available dataset in both good and good-bad weather conditions. The
proposed method's efficacy is extensively evaluated using the COCO evaluation
metric, and the best-proposed model surpasses its state-of-the-art counterpart
method by $12.55\%$ and $12.48\%$ in both good and good-bad weather conditions.
