  Semantic segmentation is crucial for autonomous driving, particularly for
Drivable Area and Lane Segmentation, ensuring safety and navigation. To address
the high computational costs of current state-of-the-art (SOTA) models, this
paper introduces TwinLiteNetPlus (TwinLiteNet$^+$), a model adept at balancing
efficiency and accuracy. TwinLiteNet$^+$ incorporates standard and depth-wise
separable dilated convolutions, reducing complexity while maintaining high
accuracy. It is available in four configurations, from the robust 1.94
million-parameter TwinLiteNet$^+_{\text{Large}}$ to the ultra-compact
34K-parameter TwinLiteNet$^+_{\text{Nano}}$. Notably,
TwinLiteNet$^+_{\text{Large}}$ attains a 92.9\% mIoU for Drivable Area
Segmentation and a 34.2\% IoU for Lane Segmentation. These results notably
outperform those of current SOTA models while requiring a computational cost
that is approximately 11 times lower in terms of Floating Point Operations
(FLOPs) compared to the existing SOTA model. Extensively tested on various
embedded devices, TwinLiteNet$^+$ demonstrates promising latency and power
efficiency, underscoring its suitability for real-world autonomous vehicle
applications.
