# Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
## 5 Applications
### Deep Learning-Based Autonomous Driving  
In supervised deep learning, a large amount of labeled data needs to be collected for training [99,100], especially in the scorching field of autonomous driving. In this field, the perception of the environment of unmanned vehicles is particularly important [101,102]. The perception of the model directly affects the quality of decision making and plays a vital role in the safety of unmanned vehicles [103,104]. However, there are many complexities environments in autonomous driving scenes. In order to ensure the performance of the model, most companies need to collect the images, point clouds, and radar data from the actual scene for training. Such massive amounts of data are challenging to collect and label. Nevertheless, active learning is able to select the most valuable samples (or via the uncertainty estimation of the current model prediction) and then manually label them. Finally, we can carry out continued model training, thereby improving the model's performance as much as possible, improving stability and security. In this section, we introduce and compare the applications on deep active learning-based autonomous driving. The overview is summarized in the Table 6.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Applications** & **Comments** & **Implementation** & **Evaluation** \\ \hline Autonomous navigation [105] & \(\rhd\) Proposed a framework for learning autonomous policies for navigation tasks from demonstrations. & \(\rhd\) Reach the flag: error rate = 2.48\%. & \(\rhd\) Follow the line: error rate = 4.06\%. \\ \hline Weather and light classification [106] & \(\rhd\) Released the first public dataset for weather and light level classification focused on autonomous driving. & \(\rhd\) Target network: Resnet18 & \(\rhd\) Weather1 [107]: accuracy = 98.80\% \\ \hline \multirow{4}{*}{3D object detection [109]} & \(\rhd\) Loss-prediction module [52]: level classification focused on autonomous driving. & \(\rhd\) Resnet2 [108]: 4 \(\times\) (GAP+FC+ReLU)+Concat+FC. & \(\rhd\) Resnet = 0.872 \\ \hline \multirow{4}{*}{Lane detection [111]} & \(\rhd\) The first work that introduced active learning into 3D object detection in autonomous driving. & \(\rhd\) Student model: ResNet-122 (for PLN [112]) ResNet-18 (for UFLD [113]) Resnet2 [114] and LLA-SENet-154 (for PLN [112]) ResNet-101 (for UFLD [113]) ResNet-101 (for UFLD [113]) ResNet-101 (for UFLD [113]) Dense-function: Combined the uncertainty and diversity metrics. & \(\rhd\) UCF-QNRF [117]: MAE = 86; MSE = 146. \\ \cline{2-3}  & \(\rhd\) The first work that used predictive uncertainty for sample selection pertaining to crowd counting task. & VGG16 & \(\rhd\) UCF-QNRF [118]: MAE = 210; MSE = 305.4. \\ \cline{2-3}  & \(\rhd\) The first work that used predictive uncertainty for sample selection pertaining to crowd counting task. & Transformer & \(\rhd\) ShanghaiTech-A [119]: MAE = 61.5; MSE = 103.4. \\ \cline{2-3}  & \(\rhd\) Proposed a partition-based sample selection with weights (PSSW) VGG16 pretrained by imagenet & \(\rhd\) ShanghaiTech-B [119]: MAE = 12.7; MSE = 20.4. \\ \cline{2-3}  & \(\rhd\)  
[PERSON] et al. [105] introduced active learning into the autonomous navigation application. In order to address the challenge of generalizing a model over unseen data, they utilized the entropy to measure the confidence of prediction and then labeled the low-confident samples for iterative training. [PERSON] et al. [106] focused on the harsh weather and low light conditions during driving. They proposed a related dataset containing 60k images from videos, which consisted of various weather conditions (clear, rain, and snow), light levels (bright, moderate, and low), and street types (asphalt, grass, and cobblestone). Under the proposed dataset, previous deep learning-based autonomous driving algorithms suffered from accuracy degradation. The authors introduced an active learning framework to reduce the redundancy from adjacent frames in the video and find the optimal subset for training.