# Monovision End-to-End Dual-Lane Overtaking Network without Map Assistance
###### Abstract  
Overtaking on a dual-lane road with the presence of oncoming vehicles poses a considerable challenge in the field of autonomous driving. With the assistance of high-definition maps, autonomous vehicles can plan a relatively safe trajectory for executing overtaking maneuvers. However, the creation of high-definition maps requires extensive preparation, and in rural areas where dual two-lane roads are common, there is little pre-mapping to provide high-definition maps. This paper proposes an end-to-end model called **OG-Net** (Overtaking Guide Net), which accomplishes overtaking tasks without map generation or communication with other vehicles. OG-Net initially evaluates the likelihood of a successful overtaking maneuver before executing the necessary actions. It incorporates the derived probability value with a set of simple parameters and utilizes a Gaussian differential controller to determine the subsequent vehicle movements. The Gaussian differential controller effectively adapts a fixed geometric curve to various driving scenarios. Unlike conventional autonomous driving models, this approach employs uncomplicated parameters rather than RNN-series networks to integrate contextual information for overtaking guidance. Furthermore, this research curated a new end-to-end overtaking dataset, **CariLaLanePass**, comprising first-view image sequences, overtaking success rates, and real-time vehicle status during the overtaking process. Extensive experiments conducted on diverse road scenes using the Carla platform support the validity of our model in achieving successful overtaking maneuvers. overtaking; dual-lane; automatic drive; feasible probability +
Footnote †: journal: Applied Sciences  
that can be used for overtaking in scenarios where there is a leading vehicle in the same lane or oncoming vehicles in a dual-lane environment. This method projects the predicted trajectories of surrounding vehicles or obstacles at time t onto a spatio-temporal coordinate system, allowing for the planning of the most suitable vehicle trajectory within feasible spatio-temporal regions. In the highway-env simulation platform, [PERSON] et al. [6] and [PERSON] et al. [7] successfully employed reinforcement learning to achieve overtaking and lane changing with relatively stable outcomes. [PERSON] et al. [8] and [PERSON] et al. [9] utilized the DQN [10] algorithm to train their model policies and optimize the selection of the best driving strategies using teacher-student networks. In addition to trajectory research, significant attention has been devoted to studying the driver's overtaking intentions. [PERSON] et al. [11] and [PERSON] et al. [12] employed statistical approaches and designed Bayesian networks to classify overtaking intentions based on the self-information of the overtaking vehicle. [PERSON] et al. [13] established upper-bound expectations on a Markov chain and adjusted the driving behavior of vehicles to choose between conservative and aggressive overtaking methods. Furthermore, [PERSON] et al. [14] and [PERSON] et al. [15] achieved overtaking through information sharing via vehicular networks. However, these studies heavily relied on high-precision maps, connected vehicles, and complex autonomous driving systems. In contrast, end-to-end methods typically do not depend on high-resolution maps [16]. At least two of the following three components are required as inputs to directly output ideal trajectory or control signals: radar signals, image data, and information about the vehicle itself. [PERSON] et al. [17] proposed \"Imitative Models\" to combine the benefits of IL and goal-directed planning. Imitative Models are probabilistic predictive models of desirable behavior that are able to plan interpretable expert-like trajectories to achieve specified goals. [PERSON] et al.