  Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art
collision avoidance systems (CAS). However, the detection of obstacles
especially during night-time is still a challenging task since the lighting
conditions are not sufficient for traditional cameras to function properly.
Therefore, we exploit the powerful attributes of event-based cameras to perform
obstacle detection in low lighting conditions. Event cameras trigger events
asynchronously at high output temporal rate with high dynamic range of up to
120 $dB$. The algorithm filters background activity noise and extracts objects
using robust Hough transform technique. The depth of each detected object is
computed by triangulating 2D features extracted utilising LC-Harris. Finally,
asynchronous adaptive collision avoidance (AACA) algorithm is applied for
effective avoidance. Qualitative evaluation is compared using event-camera and
traditional camera.
