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In typical traffic scenarios such as non-signalized roads or shared spaces where vehicles and pedestrians interact without clear separations. The interaction distance between objects is usually shorter due to the simultaneous motion of road users. Pedestrian-crossing scenarios in these areas make the scenario complex due to the unpredictability of the pedestrians intention and the need to balance between safety, comfort, and time consumption. To address this collision avoidance(CA) problem, a novel strategy using Social Force Model (SFM)-based adaptive parameters was proposed. The interaction system between the ego vehicle and the pedestrian was simplified as a Markov process to adopt the SFM-based dynamic model, and the validity of this simplification was demonstrated using real-world driving data. Based on the current state of the interaction system that consists of vehicle and pedestrian, this research adopted the optimal parameters that were generated by particle swarm optimization (PSO) to generate optimal parameters for the SFM-based vehicle dynamic model, which helps the vehicle avoid pedestrians with random motion. The proposed method was validated through bench testing, and the results showed that the proposed method balanced the safety, comfort, and time consumption requirements during the CA process in the studied scenario.

In recent years, there have been several outbreaks of extensive flooding in northern provinces. Asparagus stem blight disease and brown spot diseases have become increasingly serious, significantly reducing the yield of asparagus grown in large fields, and asparagus production has developed toward facility agriculture. To solve the problems of high labor costs, labor shortages, and low production efficiency faced by facility agriculture, the adoption of mechanized harvesting for asparagus is an inevitable trend. A prerequisite for mechanized harvesting is target detection. Compared with algorithms such as Faster R-CNN, which require the use features of candidate regions for classification and recognition and cannot meet the real-time requirements of mechanized harvesting, this paper proposes a YOLO-based asparagus recognition scheme that can quickly perform target detection with a detection accuracy of 85.45% and significantly enhanced interference resistance, which can greatly improve the production efficiency of facility agriculture and accelerate the mechanization process of facility agriculture.

Chance constrained program where one seeks to minimize an objective over decisions which satisfy randomly disturbed constraints with a given probability is computationally intractable. This paper proposes an approximate approach to address chance constrained program. Firstly, a single layer neural-network is used to approximate the function from decision domain to violation probability domain. The algorithm for updating parameters in single layer neural-network adopts sequential extreme learning machine. Based on the neural violation probability approximate model, a randomized algorithm is then proposed to approach the optimizer in the probabilistic feasible domain of decision. In the randomized algorithm, samples are extracted from decision domain uniformly at first. Then, violation probabilities of all samples are calculated according to neural violation probability approximate model. The ones with violation probability higher than the required level are discarded. The minimizer in the remained feasible decision samples is used to update sampling policy. The policy converges to the optimal feasible decision. Numerical simulations are implemented to validate the proposed method for non-convex problems comparing with scenario approach and parallel randomized algorithm. The results show that proposed method have improved performance.