Automotive Collision Warning System Research Articles

Compressed Level Crossing Sampling for Ultra-Low Power IoT Devices

Level crossing sampling (LCS) is a power-efficient analog-to-digital conversion scheme for spikelike signals that arise in many Internet of Things-enabled automotive and environmental monitoring applications. However, LCS scheme requires a dedicated time-to-digital converter with large dynamic range specifications. In this paper, we present a compressed LCS that exploits the signal sparsity in the time domain. At the compressed sampling stage, a continuous-time ternary encoding scheme converts the amplitude variations into a ternary timing signal that is captured in a digital random sampler. At the reconstruction stage, a low-complexity split-projection least squares (SPLSs) signal reconstruction algorithm is presented. The SPLS splits random projections and utilizes a standard least squares approach that exploits the ternary-valued amplitude distribution. The SPLS algorithm is hardware friendly, can be run in parallel, and incorporates a low-cost k-term approximation scheme for matrix inversion. The SPLS hardware is analyzed, designed, and implemented in FPGA, achieving the highest data throughput and the power efficiency compared with the prior arts. Simulations of the proposed sampler in an automotive collision warning system demonstrate that the proposed compressed LCS can be very power efficient and robust to wireless interference, while achieving an approximately eightfold data volume compression when compared with Nyquist sampling approaches.

Gaussian mixture approach to decision making for automotive collision warning systems

This paper proposes a practical probabilistic approach to collision decision making which is necessary for advanced automotive collision warning system (CWS) using FMCW radar. Most decision making algorithms assess the probable collisions based on the predicted collision position which is usually expressed as a nonlinear function of threat vehicle’s position and velocity provided by FMCW radar. Since the predicted collision position has highly nonlinear statistics in general, it is one of main obstacles to improving the reliability of the collision probability computation and to developing real-time decision making algorithms. This motivates us to devise a Gaussian mixture method for collision probability calculation with the help of linear recursive time-to-collision (TTC) estimation. The suggested TTC estimator provides an accurate TTC estimate with small estimation error variance hence it enables us to approximate the probability density function of the predicted collision position as the weighted sum of just a few Gaussian distributions. Therefore, our approach could drastically reduce the inherent nonlinearity of collision decision making problem and computational complexity in collision probability calculation. Through the simulations for the typical engagement scenarios between the host and threat vehicles, the performance and effectiveness of the proposed algorithm is compared to those of the existing ones which require heavy computational burden.

Study on Safe Distance for the Automotive Collision Warning System Based on Human-Vehicle-Environment

In order to improve the effectiveness and practicability of the car anti-collision system, a model of the safety distance between two cars took into account human, vehicle and environment was presented in the paper. Firstly, a general automobile safety distance calculation model based on the braking process was derived, and an algorithm of estimating safe running state on vehicle was presented, which includes the comprehensive factors that influence the vehicle safety travel. Then the general automobile safety distance model was corrected and regulated according to the state of driving safety. At last, the method was verified by the simulation experiments. Experimental results indicate that the algorithm was more consistent with the driver individual characteristic and the actual situation of vehicle and road, and the driver had the most impact.

面向汽车防撞的混沌激光雷达

面向汽车防撞的混沌激光雷达