In recent years, the development of Advanced Driver-Assistance Systems (ADASs) is driving the need for more reliable and precise on-vehicle sensing. Radar and lidar are crucial in this framework, since they allow sensing of vehicle’s surroundings. In such a scenario, it is necessary to master these sensing systems, and knowing their similarities and differences is important. Due to ADAS’s intrinsic real-time performance requirements, it is almost mandatory to be aware of the processing algorithms required by radar and lidar to understand what can be optimized and what actions can be taken to approach the real-time requirement. This review aims to present state-of-the-art radar and lidar technology, mainly focusing on modulation schemes and imaging systems, highlighting their weaknesses and strengths. Then, an overview of the sensor data processing algorithms is provided, with some considerations on what type of algorithms can be accelerated in hardware, pointing to some implementations from the literature. In conclusion, the basic concepts of sensor fusion are presented, and a comparison between radar and lidar is performed.

The rapid advancement of artificial intelligence in automotive applications, particularly in Advanced Driver-Assistance Systems (ADAS) and smart battery management on electric vehicles, increases the demand for efficient near-sensor processing. While the problem of linear algebra in machine learning is well-addressed by existing accelerators, the computation of nonlinear activation functions is usually delegated to the host CPU, resulting in energy inefficiency and high computational costs. This paper introduces TOXOS, a RISC-V-compliant coprocessor designed to address this challenge. TOXOS implements the COordinateRotation DIgital Computer (CORDIC) algorithm to efficiently compute nonlinear functions. Taking advantage of RISC-V modularity and extendability, TOXOS seamlessly integrates with existing computing architectures. The coprocessor’s configurability enables fine-tuning of the area-performance tradeoff by adjusting the internal parallelism, the CORDIC iteration count, and the overall latency. Our implementation on a 65nm technology demonstrates a significant improvement over CPU-based solutions, showcasing a considerable speedup compared to the glibc implementation of nonlinear functions. To validate TOXOS’s real-world impact, we integrated TOXOS in an actual RISC-V microcontroller targeting the on-vehicle execution of machine learning models. This work addresses a critical gap in transcendental function computation for AI, enabling real-time decision-making for autonomous driving systems, maintaining the power efficiency crucial for electric vehicles.

This paper introduces TestIt, an open-source Python package designed to automate full-system integration testing using a Software-Based Self-Test (SBST) approach. By dynamically generating test vectors and golden references, TestIt significantly reduces development time and complexity while supporting both simulation and FPGA environments. Its flexible design positions TestIt as a key enabler for the widespread adoption of CI/CD methodologies in open-source RTL development. A case study on the X-HEEP RISC-V microcontroller (MCU), which integrates a custom accelerator, showcases TestIt's ability to detect hardware and software faults that traditional formal methods may overlook. Furthermore, the case study highlights how TestIt can be leveraged to characterize system performance with minimal effort. By automating testing on the PYNQ-Z2 FPGA development board, we achieved a 11x speed-up with respect to RTL simulations.