Current camera-based tracking-by-detection methodologies in the literature overlook the significance of detection box localization confidence, generally assuming that objects with low detection confidence are highly occluded and, therefore, either ignored or deprioritized in the matching process. Furthermore, appearance similarity is typically neglected when matching these low-confidence objects. This oversight presents a critical research gap as objects with low detection confidence might still exhibit a clear appearance, and those with high detection confidence can present imprecise localization or ambiguous appearance, factors not accounted for in existing methods. To address this gap, our work contributes a novel framework, Localization-Guided Track (LG-Track), which, for the first time, integrates localization confidence of camera detections in multi-object tracking (MOT). LG-Track takes into account both appearance clarity and localization precision of detection boxes, incorporating a novel deep association mechanism that enhances tracking performance. Based on the localization and classification confidence of detection boxes, different cost matrices are employed in different levels of the proposed deep association mechanism to achieve enhanced matching accuracy. Our method, validated through rigorous experimentation on the MOT17 and MOT20 benchmarks, demonstrates superior performance over current compared state-of-the-art tracking methods. Committed to furthering research in this field, we have made our code accessible to the community at https://github.com/mengting2023/LG-Track .

Transmission error (TE) and backlash are important parameters used to evaluate the transmission accuracy of cycloid-pin drives. Existing calculation methods are mostly based on two-dimensional tooth profile models, and these methods ignore the influence of some abnormal meshing phenomena caused by profile modifications (PMs), manufacturing errors (MEs), and assembly errors (AEs), such as the instantaneous mesh-apart of tooth pairs and the eccentric load on the tooth surface. To fill this gap, a novel approach to accurately calculating the TE and backlash of a cycloid-pin gear pair based on the error tooth surfaces is proposed, and its feasibility and effectiveness are validated by comparison with the theoretical analyses and the results from the literature. Based on this, the effects of the PMs, MEs, and AEs on the transmission accuracy are studied, which will be helpful in optimizing the tooth profile design of a cycloid gear and the tolerance allocation during the installation of a gear pair. The proposed method is also expected to provide accurate error excitation data for the dynamic analysis of cycloid-pin drives.

Designing a secure and reliable decision-planning model for vehicle lane changing is of utmost practical significance because it is one of the most frequent driving behaviors and has a substantial impact on the safety of drivers’ lives and property. First, a Gaussian mixed Hidden Markov model (GMHMM) is trained for lane change intention recognition (LCIR), and the results reveal that the model has a great performance. This will simplify the game process and provide drivers and passengers with warnings. Second, the safety, efficiency, and comfort payoffs of vehicle lane changes are taken into account when building the game model. When building the safety payoff function, temporal collision risk and spatial collision risk of vehicles are two of them that are carefully taken into account. After that, the vehicle’s trajectory tracking control is decoupled into lateral LQR + feedforward control and longitudinal dual proportional integral derivative (PID) control based on the Frenet coordinate system. Finally, a vehicle lane change scenario is built for simulation analysis, and the effects of driving comfort factor and driving efficiency factor on lane change results are considered. The results show that the proposed game theory lane change model ensures lane change safety while satisfying human drivers’ requirements for lane change efficiency and comfort.