## 1 Introduction  
Recent studies on intelligent vehicles have advanced rapidly with the development of deep learning-based computer vision techniques and much attention to self-driving vehicles. Visual perception is playing a key role in this revolution. It enables the vehicle to understand the driving environments and estimate drivable areas. The drivable area estimation for safe driving requires a more high-level understanding than other tasks such as free space detection, vehicle detection, pedestrian detection, and lane line detection [1]. According to the current environment in real-time, a drivable area is necessarily divided into ego-lanes, other drivable lanes, sidewalks, and so forth [2]. Although many methods have shown successful results in drivable area estimation [3], it is not straightforward to apply these methods to real-world applications. Knowing the position of the lane line is essential to move the vehicle correctly on the road and avoid traffic accidents with others. Single tasks, such as lane line segmentation, object detection and semantic segmentation, alone, are insufficient to decide road affordability of driving because many areas have different conditions such as the absence of lanes [4]. Furthermore, the road is shared with all other vehicles. If the lane is occupied by an obstacle or the free space of other vehicles coming from the opposite direction, it should also be considered. Thus, understanding drivable areas to set the proper path where the ego vehicle can drive is an essential function for safe driving. Although most studies have focused on improving performance to address practical real-world autonomous driving applications that have limited resources, reducing computational cost is also a vital factor. In particular, as safety is vital for autonomous driving, the required perception tasks for autonomous driving are diversified, whereas resources for understanding them are limited. To address this issue, herein, we adopt the multi-task learning approach to small deep network architecture to understand the environmental context of current driving vehiclesand reduce the computation for real-time inference. This network has desirable properties, such as low-power usage, real-time processing, and small model size while maintaining competitive accuracy. Multi-task learning can improve learning efficiency and prediction accuracy by learning multiple objectives from a shared representation [5]. Furthermore, the multi-task learning approach allows the model to perform in real-time by combining all loss functions to simultaneously learn multiple objectives in a single small model from shared computations. For elaborate estimation of drivable areas, we performed three tasks simultaneously as follows: (1) drivable area estimation, that pixel-wise classification of road that vehicles can forward; (2) lane line segmentation, that detecting lane lines to divide directly and alternatively drivable area, and (3) scene classification, that is, understanding the global context of current driving environments. Figure 1 shows an input image and three tasks for drivable area estimation. In Figure 0(b), the red color region corresponds to the ego-lane and blue color region indicates the alternative drivable lane. In Figure 0(b), the red color region corresponds to the ego-lane and the blue color region indicates the alternative drivable lane. The drivable area is more likely around the lane lines. Therefore, a unified structure that shares the representation can achieve not only better computation efficiency, but also a better performance than separate architecture. In this paper, we propose a fast, simple, efficient, and accurate end-to-end neural network architecture to enhance the simultaneous estimation of drivable areas, lane line segmentation, and scene classification based on a multi-task learning approach. We focus on computational times with competitive performances to enable safety-critical real-time applications.