Physical Removal Attack On LiDAR Sensors And Mitigation Strategies

A technical paper titled "You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks" was published by researchers at University of Michigan, University of Florida and the University of Electro-Communications (Japan). This paper was included at the recent 32nd USENIX Security Symposium. Abstract: "Autonomous Vehicles (AVs) increasingly use LiDAR-base... » read more

Object Detection CNN Suitable For Edge Processors With Limited Memory

A technical paper titled “TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers” was published by researchers at ETH Zurich. Abstract: "This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrol... » read more

Issues And Challenges In Super-Resolution Object Detection And Recognition

If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

Weird Incidents Reveal L5 Challenges

A series of surprising, counterintuitive, and sometimes bizarre incidents reveal the challenges of achieving full Level 5 autonomy in self-driving vehicles, which are an increasingly common site in major cities. While it’s easy to dismiss such anecdotes as humorous glitches compared with the sobering accounts of autonomous tech-related injuries and fatalities, industry executives say these oc... » read more

Data Fusion Scheme For Object Detection & Trajectory Prediction for Autonomous Driving

New research paper titled "Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving" from researchers at Uber. 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 featur... » read more

A Novel Power-Saving Reversing Camera System with Artificial Intelligence Object Detection

Abstract "According to a study by the Insurance Institute for Highway Safety (IIHS), the driving collision rate of using only the reversing camera system is lower than that of using both the reversing camera system and the reversing radar. In this article, we implemented a reversing camera system with artificial intelligence object detection to increase the information of the reversing image... » read more

Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data

Abstract "In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a bro... » 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

Enhancement of Robustness in Object Detection Module for Advanced Driver Assistance Systems

Abstract: "A unified system integrating a compact object detector and a surrounding environmental condition classifier for enhancing the robustness of object detection scheme in advanced driver assistance systems (ADAS) is proposed in this paper. ADAS are invented to improve traffic safety and effectiveness in autonomous driving systems where object detection plays an extremely important rol... » read more

Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems

Abstract "This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challengin... » read more

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