Driving risk assessment and prevention strategies for autonomous vehicle in open-pits

Driving risk assessment and protection is the critical technology of unmanned transportation systems in open-pits. In order to warrant the safe operation of unmanned vehicles in open-pits, the Driving Security Model (DSM) based on the vehicle-road-cloud transportation system is established. Based on the multi-source information from the vehicle, roadside, and cloud platform, the DSM can assess the driving risk level of driverless vehicles and provide corresponding driving risk prevention strategies. The DSM comprises driving state awareness, driving risk assessment, and driving risk protection. In terms of driving risk assessment, the threshold of pre-collision time is corrected through the road slope ahead of the vehicle, and the minimum braking safety distance is modified by the information of road slope and vehicle load state. In the meantime, a comprehensive driving risk assessment strategy is proposed, which can quantify the real-time collision risk of autonomous vehicles in open-pits. Then, a collision risk protection system that considers different driving risks is then designed based on a finite state machine. A smooth braking control strategy is developed to meet the minimum safety distance. Finally, a digital twin simulation system that corresponds to the autonomous vehicle in an open-pit is built based on the PreScan and Matlab co-simulation technology and some simulation tests in the horizontal, uphill-downhill road and full load scenes are carried out. The simulation results show that the DSM’s comprehensive risk assessment strategy can evaluate suitable risk levels in advance and timely brake, which indicates that the introduction of road slope information can improve the driving safety of the vehicle up and downhill scenes. By introducing vehicle load information, the designed minimum safe braking distance index can detect potential collision risk in time. The DSM’s emergency braking control strategy can smoothly stop the vehicle before 10 m safe distance, which improves the stability of heavy-duty vehicles during emergency braking

Comprehensive quality evaluation of Chishao by HPLC

Objective: The purpose of this paper is to comprehensively evaluate the quality of Chishao. Methods: In the experiment of this paper, the fingerprint spectrums of Chishao in all locations are established by RP-HPLC and the model of principle component analysis with the RP-HPLC peak areas is established. Results: The quality of Chishao in the northern part of China or that made of Paeonia lactiflora is better than that of these in others or that made of other species. The quality of Chishao comes from P. veitchii is in the middle class and is better than those that comes from P. obovata, P. mairei and P. anomala. The results are consistent with traditional views of the quality of this plant. These results indicates that principal component analysis (PCA) can be used as an effective and economic method to evaluate the quality of Chishao, and may be extended to other Chinese medicinal plants. Conclusions: Due to the complex basis of the efficacy of Traditional Chinese Medicine (TCM), the method such as PCA of several chemical components appears to be a more appropriate method for the quality evaluation of TCM in contrast to the determination of a single or few chemicals

Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm

In order to solve the problem of unmanned truck transportation scheduling in open-pit mines, the minimum sum of fuel cost, fixed start-up cost, breakdown maintenance cost, and network base station construction and maintenance cost are taken as the objective functions. The mining amount of mining station, crushing amount of crushing station, truck number and truck transportation workload are taken as the constraint conditions. The optimization model of unmanned truck transportation scheduling in open-pit mines is established. To solve the problem of imbalance between global exploration and local mining ability in the tunicate swarm algorithm, an improved tunicate swarm algorithm (ITSA) based on Singer mapping and adaptive updating mechanism of parameter position is proposed. And it is applied to solve the optimization model of unmanned truck transportation scheduling in open-pit mines. Singer mapping is introduced to enhance the distribution of the initial tunicate swarm in the solution space and accelerate the compression of the solution space, thus improving the convergence speed of the algorithm. Through the adaptive updating mechanism of parameter position, the positions of the tunicate and the optimal tunicate are adjusted to increase the search range of the solution space. Therefore, the algorithm jumps out of the local optimization. The simulation results show that ITSA has better convergence precision, convergence speed and stability compared with the four population intelligent optimization algorithms of grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA), atom search optimization algorithm (ASO) and tunicate swarm algorithm (TSA). In the unimodal benchmark function, the evaluation indexes of ITSA are far better than those of the other four algorithms, which shows that ITSA has better local mining capacity. In the multi-peak benchmark function, the evaluation indexes of ITSA show better optimization performance, which indicates that ITSA has better global exploration performance. The practical application scenario verification shows that ITSA has faster convergence speed and higher convergence precision when used for solving the unmanned truck transportation scheduling optimization model. And ITSA reduces the truck transportation cost and transportation distance