An autonomous vehicle generates several terabytes of sensor data per day. A significant portion of this data consists of 3D point clouds produced by depth sensors such as LiDAR. This data is transferred to cloud storage, where it is utilized for training machine learning models or conducting analyses, e.g., forensic investigations in the event of an accident. To reduce network and storage costs, this paper introduces DejaView that searches for and uses redundancies on larger temporal scales (days and months) for more effective compression. We designed DejaView with the insight that the operating area of autonomous vehicles is limited and that vehicles mostly traverse the same routes daily. Consequently, the daily collected 3D data is likely similar to the data they’ve captured in the past. To capture this, the core of DejaView is a diff operation that compactly represents point clouds as delta w.r.t. 3D data from the past. Using two months of LiDAR data, DejaView can compress point clouds by a factor of 210 at a reconstruction error of only 15 cm.

Autonomous vehicles and human drivers are prone to line-of-sight limitations. Road-side mounted 3D sensors like LiDARs can augment a vehicle's on-board perception. However, this entails fusing 3D frames at low latency and high accuracy. Road-side and vehicle 3D frames are captured from different viewpoints. This adversely affects alignment accuracy and can be computationally expensive. To this end, VRF optimizes for both latency and accuracy by decoupling the alignment process into indirect and direct alignments. First, VRF indirectly aligns the 3D frames by aligning them to a common reference point i.e., a vehicle's on-board 3D map. Then, it directly aligns the two point clouds to refine this alignment. To ensure high accuracy, it incorporates novel offline registration and alignment accuracy forecasting modules. To ensure low latency, it uses a fast fusion pipeline that caches previous and offline computations. To our knowledge, VRF is the first vehicle road-side cooperative system to ensure cm-level accuracy and end-to-end latency less than 20 ms. Most importantly, its latency is below the 100 ms threshold required for autonomous vehicles to react to external events. Finally, VRF can improve reaction time to external events by as much as 5 seconds1.