  Modern vehicles are equipped with various driver-assistance systems,
including automatic lane keeping, which prevents unintended lane departures.
Traditional lane detection methods incorporate handcrafted or deep
learning-based features followed by postprocessing techniques for lane
extraction using frame-based RGB cameras. The utilization of frame-based RGB
cameras for lane detection tasks is prone to illumination variations, sun
glare, and motion blur, which limits the performance of lane detection methods.
Incorporating an event camera for lane detection tasks in the perception stack
of autonomous driving is one of the most promising solutions for mitigating
challenges encountered by frame-based RGB cameras. The main contribution of
this work is the design of the lane marking detection model, which employs the
dynamic vision sensor. This paper explores the novel application of lane
marking detection using an event camera by designing a convolutional encoder
followed by the attention-guided decoder. The spatial resolution of the encoded
features is retained by a dense atrous spatial pyramid pooling (ASPP) block.
The additive attention mechanism in the decoder improves performance for high
dimensional input encoded features that promote lane localization and relieve
postprocessing computation. The efficacy of the proposed work is evaluated
using the DVS dataset for lane extraction (DET). The experimental results show
a significant improvement of $5.54\%$ and $5.03\%$ in $F1$ scores in multiclass
and binary-class lane marking detection tasks. Additionally, the intersection
over union ($IoU$) scores of the proposed method surpass those of the
best-performing state-of-the-art method by $6.50\%$ and $9.37\%$ in multiclass
and binary-class tasks, respectively.
