The Road Ahead For SoCs In Self-Driving Vehicles

Automakers have relied on a human driver behind the wheel for more than a century. With Level 3 systems in place, the road ahead leads to full autonomy and Level 5 self-driving. However, it’s going to be a long climb. Much of the technology that got the industry to Level 3 will not scale in all the needed dimensions — performance, memory usage, interconnect, chip area, and power consumption... » read more

Designing Crash-Proof Autonomous Vehicles

Autonomous vehicles keep crashing into things, even though ADAS technology promises to make driving safer because machines can think and react faster than human drivers. Humans rely on seeing and hearing to assess driving conditions. When drivers detect objects in front of the vehicle, the automatic reaction is to slam on the brakes or swerve to avoid them. Quite often drivers cannot react q... » read more

Streamlining Vehicular Electrical System Design And Verification

Automobiles and other road vehicles such as trucks and buses have always been one of the most demanding applications for mechanical and electrical design. Systems must operate properly over a wide range of environmental conditions, facing extremes of temperature, humidity, sunlight, dirt, vibration and more. User expectations of reliability and availability mandate safety-critical design practi... » read more

Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving

Abstract "We present an end-to-end method for object detection and trajectory prediction utilizing multi-view representations of LiDAR returns. Our method builds on a state-of-the-art Bird's-Eye View (BEV) network that fuses voxelized features from a sequence of historical LiDAR data as well as rasterized high-definition map to perform detection and prediction tasks. We extend the BEV network ... » read more