# Monovision End-to-End Dual-Lane Overtaking Network without Map Assistance
###### Abstract
[18] embedded spatial and temporal attention mechanisms based on ConvLSTM and SE-Net modules into appropriate layers to focus on key visual information and long-term and short-term memory in visual sequences in order to predict the steering angle of the ego vehicle. This contributed to subsequent research on the mapping of raw inputs and control signals. [PERSON] et al. [19] proposed the Openpilot model, capable of achieving L2-level autonomous driving functions. [PERSON] et al. [20] proposed an innovative uncertainty-aware framework for end-to-end control, utilizing the probabilistic control barrier function (CBF) to enforce safety constraints. They experimented on a small race car and achieved better cornering performance. The TCP model [21] developed by the Shanghai Artificial Intelligence Lab utilizes only first-person view images and vehicle information as inputs. It can guide vehicle control using generated trajectories and achieved first place in the 2022 Carla leaderboard. Currently, these end-to-end models are capable of achieving Level 2 and Level 3 autonomous driving functions. Some research has employed end-to-end models to generate data required for overtaking, such as trajectory prediction. [PERSON] et al. [22] developed IOAS, which provides overtaking suggestions to drivers. This system predicts the speed of the leading vehicle by combining VNet and TTC-Net networks, ensuring high accuracy. Similarly, [PERSON] et al. [23] utilized the tail lights of the leading vehicle and the headlights of oncoming vehicles to determine the speed information of relevant vehicles in night-time environments, providing overtaking recommendations. Nevertheless, the majority of studies in the field concentrate on conservative driving tasks [21]. These models are trained based on correct control signals or planned trajectories as labels, but the data generated by traffic accidents is actually equally important. Including these data in model training can help improve the ability of self-driving cars to avoid dangers. Currently, there is no denying that the end-to-end model is capable of executing simple driving tasks on certain simulation platforms. However, its ability to handle more complex and risky maneuvers, such as overtaking on dual-lane roads, is still a thought-provoking issue. Thus, an effective end-to-end model for overtaking on roads should have the capacity to perform overtaking maneuvers when safe and quickly return to the main lane when potential hazards are detected. In light of these considerations, this paper presents a model capable of estimating the probability of safe overtaking in real time using the information readily available from the vehicle (e.g., image, speed, and steering wheel angle). By utilizing this probability to make an overtaking decision, an overtaking control scheme is generated. In accomplishing this, our contributions are as follows:  
* This paper proposes an innovative method to address the relatively perilous challenge of overtaking in autonomous driving, substantiating the viability of the end-to-end approach in tackling this task. * This paper abandons the traditional tracking control method, and we design a control planner that can drive an overtaking curve with fixed lateral displacement at different speeds using geometric curves. * This study established a dataset for dual-lane overtaking scenarios called CarlaLanePass, encompassing both successful and unsuccessful overtaking situations. CarlaLanePass offers a distinctive perspective that contributes to the realm of autonomous driving research.