Autonomous vehicle (AV technology) relies heavily on vision based applications like object recognition, obstacle/collision avoidance etc. In order to achieve this, understanding and estimating the dynamics in the environment is extremely important. LIDARs are proven to detect both shape as well as the speed/movement of the objects in the scene but one of the biggest challenges faced in adapting LIDAR technology is the huge amount of data it produces and the way it is processed. Most of this data is redundant static information which results in wastage of system memory, computational resources, power and time. Inspired from biological retina, first Neuromorphic-Retina for LIDAR is proposed that is able to extract and encode movement happening at particular distance, particular angle and with particular velocity from raw LIDAR temporal pulses into unique spike sequences so that the information about the dynamic environment can be efficiently classified and processed by event based and low powered Neuromorphic processing unit. The system is designed in such a way that it avoids consumption of large amount of computational resources and system memory. Simulation results show that the Retina is able to filter out redundant static information from the LIDAR data stream thereby reducing data throughput of around 50 - 70 % with 5 - 22 % spatial quality loss (based on scenario) as well as remove noise caused due to luminous reflections. This has tremendous impact on system latency and power consumption due to drop in memory accesses.