  Several popular computer vision (CV) datasets, specifically employed for
Object Detection (OD) in autonomous driving tasks exhibit biases due to a range
of factors including weather and lighting conditions. These biases may impair a
model's generalizability, rendering it ineffective for OD in novel and unseen
datasets. Especially, in autonomous driving, it may prove extremely high risk
and unsafe for the vehicle and its surroundings. This work focuses on
understanding these datasets better by identifying such "good-weather" bias.
Methods to mitigate such bias which allows the OD models to perform better and
improve the robustness are also demonstrated. A simple yet effective OD
framework for studying bias mitigation is proposed. Using this framework, the
performance on popular datasets is analyzed and a significant difference in
model performance is observed. Additionally, a knowledge transfer technique and
a synthetic image corruption technique are proposed to mitigate the identified
bias. Finally, using the DAWN dataset, the findings are validated on the OD
task, demonstrating the effectiveness of our techniques in mitigating
real-world "good-weather" bias. The experiments show that the proposed
techniques outperform baseline methods by averaged fourfold improvement.
