# Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process
###### Abstract  
A vision-based lane detection system is difficult to implement in vehicles due to the unique characteristics of algorithms, neural network architectures, limitations, and strict hardware/software requirements. This paper proposes a new architecture, called hybrid U-Net, which represents a significant extension of the classical U-Net model, addressing its limitations in capturing global relationships, adaptability to scale variations, and sensitivity to geometric structures. An important aspect of this new architecture is the balance between performance and computational efficiency, maintaining reasonable values of performance parameters with a competitive inference time. The experimental results show that hybrid U-Net achieved superior scores in both mIoU and F1 score parameters. The most notable increase was observed in the (TuSimple+Carla) dataset, where mIoU increased from 0.56 to 0.60 (+7%), and F1 score from 0.69 to 0.74 (+7.2%). Similarly, in the Carla dataset (known for the complexity of the generated images), a substantial improvement was recorded, with an increase of +8.0% in mIoU and +5.7% in F1 score, showing better adaptability of the model to geometric structures in complex images. However, these improvements come at a considerable computational cost, as the inference time of the hybrid U-Net model is approximately 0.04 s per image (compared to 0.016 s per image for U-Net), resulting in a decrease in FPS from 60 to approximately 23. Future research should investigate model compression techniques, such as knowledge distillation, pruning, or quantization, to improve efficiency without sacrificing accuracy. A 2025 13 June 2025 9 July 2025 9 July 2025 15 July 2025 13 June 2025 9 July 2025 17 July 2025 13 June 2025 9 July 2025 15 July 2025 13 July 2025 13 July 2025 13 June 2025 9 July 2025 15 July 2025 15 July  
* Reducing human errors (driver alerting and proactive intervention). * Increasing road safety--one of the main reasons for the massive acceptance and integration of ADAS systems in the automotive market (by reducing the number of accidents and implicitly the number of injuries and deaths, increasing the safety of other road users--pedestrians, cyclists). * Improving comfort and convenience at the wheel through ADAS systems that can automatically adjust the vehicle speed depending on traffic conditions (ACC), provide assistance in staying in the lane (LKA and LCA), facilitate or automate parking maneuvers, and support traffic jam driving by managing slow traffic with numerous stops and starts. These systems reduce driver stress and fatigue and may indirectly contribute to lowering pollutant emissions through smoother and more efficient driving behavior [5]. * Providing the basic foundation for the development of fully autonomous vehicles. As ADAS systems become more sophisticated and highly integrated, they pave the way for higher levels of vehicle automation. Due to the proven safety benefits of ADAS systems, regulatory bodies have introduced (and continue to introduce) mandates that accelerate their adoption by consumers and drive rapid development and implementation efforts by automotive manufacturers [6]. The effects of ADAS systems are reflected in a significant reduction in accidents and fatalities. Forward collision warning (FCW) and automatic emergency braking (AEB) systems have been shown to significantly reduce rear-end collisions, and lane departure warning (LDW) and lane keeping assist (LKA) systems have reduced lane departure-related accidents [7]. In terms of economic benefits, these are mainly found in reduced repair costs (due to fewer accidents) and in reduced fuel consumption and pollutant emissions (adaptive pilotage can optimize driving patterns, leading to more consistent speeds and potentially better fuel economy). In the transportation industry, commercial fleets of road transport vehicles have increased operational productivity by reducing/eliminating downtime due to repairs caused by accidents and savings in fuel consumption.