Automotive Adaptive Cruise Control Research Articles

Hardware Loop Simulation of Distributed Embedded Integrated Circuits

In order to improve the performance of the automotive active safety system, the author proposes a distributed embedded ACCS hardware loop simulation system. The automotive adaptive cruise control system (ACCS) is a completely distributed embedded control system. The author adopts the method of hardware loop simulation, an ARM-based automotive ACCS simulation platform is designed, and the hardware structure and software design of the platform are introduced. The simulation results show that the performance of single-node and double-node responses to step change is basically the same. There is a network in the two-node control loop, the carrying capacity and communication bandwidth of the network are limited, which will inevitably cause information collision and retransmission, which will inevitably cause a delay in the information transmission process, with a delay of about 4.5 seconds. The system opens up the design idea of ACCS and also lays a foundation for future research in related directions, which has a certain enlightenment effect.

A framework of safety analysis with temporal feature based on MBSA and case study for ACC system

The safety of automotive Adaptive Cruise Control (ACC) system is of great significance to prevent fatigue driving, improve driving comfort, reduce accident rate and promote the development of intelligent transportation and autonomous driving technology. However, the current safety analysis of ACC lacks consideration of the temporal dynamic property, so it is necessary to establish a set of safety analysis methods to consider the temporal characteristics. This paper proposes a new safety analysis method based on MBSA framework and introduces temporal features. Altarica3.0 is a high-level modeling language for safety analysis, and its basic mathematical form is Guardian Transformation System (GTS). In this paper, we outline an analysis approach that converts failure behavioral models (GTS) to temporal fault trees (TFTs), which can be analyzed using Pandora a recent technique for introducing temporal logic to fault trees. However, like classical fault tree analysis, TFT analysis requires a lot of manual effort, which makes it time consuming and expensive. In order to improve the safety of the system, the proposal extends Bayesian Networks with Pandora and results to dependability analysis with temporal relationships to provide more reliable basis for safety design. As a typical case study, the safety analysis method proposed in this paper is applied to the safety analysis of adaptive cruise system, and the results show the effectiveness of the proposed method. Furthermore, it also provides new technologies for the automation and intelligence of safety analysis for smart internet of vehicle.

MATLAB-Based GPU Acceleration for Multiple-Input and Multiple-Output Radar Beamforming Algorithm

Multi-input and multi-output (MIMO) radar systems possess numerous advantages, such as scanning an entire region much faster than a phased array, enhancing the spatial resolution, mitigating interference and multipath fading, and improving the probability of detection of targets. MIMO radar systems use adaptive beamforming techniques such as the minimum variance distortionless response (MVDR) to obtain the best possible estimation of the direction of arrival (DOA) of the targets. The MVDR algorithm has been thoroughly investigated for traditional phased array radars. For MIMO radar systems, however, it requires significant signal processing, which can introduce substantial latency, especially in the case of large MIMO systems where few hundred antennas are used; this can be solved by using a Graphics Processor Unit (GPU), which contains thousands of cores and can execute many operations in parallel. This paper presents a MATLAB-based approach for GPU parallelization of the minimum variance distortionless (MVDR) beamforming algorithm in a MIMO radar system. Two MIMO radar systems are considered. The first one is a simulated MIMO radar which is used for automotive adaptive cruise control (ACC), and the second one is an experimental monostatic MIMO radar that is based on a vector network analyzer (VNA). It is shown that the GPU achieved a speedup of up to 7 times while successfully detecting all targets.