Physics-Based Radar Modeling: Driving Toward Increased Safety

Autonomous driving is revolutionizing the global automotive industry. With every new model, cars are smarter and more capable of independently responding to external signals like lane markings, road signs, other cars and pedestrians. However, formulating a correct response via artificial intelligence depends on the flawless performance of the car’s perception systems, including radar-ba... » read more

Verify Perception Systems Virtually Via Accurate Fog Models

Under every lighting level and weather condition, cameras must reliably “see” pedestrians and other physical objects ― and trigger an appropriate reaction from critical systems such as braking. This is especially challenging in foggy conditions, which can confuse visual cameras, radar, lidar and other conventional sensor technologies. Thermal imaging represents a potential solution, helpi... » read more

Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS

Abstract: "Camera-based Deep Learning algorithms are increasingly needed for perception in Automated Driving systems. However, constraints from the automotive industry challenge the deployment of CNNs by imposing embedded systems with limited computational resources. In this paper, we propose an approach to embed a multi- task CNN network under such conditions on a commercial prototy... » read more