dataesr/scientific-paragraphs-categorization huggingface dataset:
The performance of the controller was evaluated by comparing its steering behavior with that of a human driver. In  [40] , the study detailed the training of an end-to-end model for controlling the lateral motion of autonomous vehicles (AVs). The authors demonstrated that CNNs can drive in similar but previously unseen scenarios. To train the network, a small amount of training data was used within a simulation to control the car. Furthermore, the study suggests that this approach can be extended to integrate longitudinal control of AVs by training the network to use both steering angles and vehicle speed to achieve the desired steering angles and vehicle speed. An end-to-end autonomous driving system that combines long short-term memory (LSTM) and CNN architectures was proposed in  [41]. The integration of LSTM and CNN allowed the system to extract both spatial and temporal features, and changes in the steering-wheel angle of the vehicle were considered over time.
However, data collection from real-world vehicles was limited due to the challenges in processing vehicle data. To overcome this issue, the authors used a driving simulator to train the proposed approach for the steering wheel angle. The study suggests that a complete end-to-end driving system could be constructed using large-scale vehicle data, provided solutions to the limitations in vehicle data collection are found. In  [42] , a deep neural network technique was proposed to explore the potential of event cameras in predicting the steering angle of a vehicle, which is a challenging task in motion estimation. The performance of this approach was evaluated on a publicly available dataset of large-scale event-camera data. The simulation results demonstrated that the proposed approach performs well using event cameras under different conditions, including challenging illumination and fast motion, compared to traditional standard cameras.
In  [43] , a novel network architecture was developed to enhance the end-to-end learning of the steering angle based on baseline vision using auxiliary tasks. The architecture incorporated vehicle kinematics, recurrence modules, segmentation of auxiliary image, introduction of optical flow, and transfer from existing tasks as distinct auxiliary tasks. The Imagenet recognition task was used to learn features that were useful for on-road driving in the steering task. By enhancing the mask of the pre-trained segmentation with more information, a more accurate prediction could be obtained. Additionally, the precise addition of vehicle kinematics led to a more concrete representation of the state and boosted performance. The proposed approach was evaluated using the Comma. ai dataset and Udacity simulator, demonstrating its effectiveness in improving end-to-end learning of the steering angle.
An advanced chauffeur hybrid method was utilized to steer autonomous vehicles in various situations, including changing from one road to another, lane changes, traffic signs, and traffic lights  [44]. CNNs were used to extract data features from the Udacity simulator, and both recurrent neural networks (RNNs) and CNNs were employed. However, one limitation of this method is that it does not consider other road users or obstacles, which can pose significant challenges for autonomous vehicles in real-world driving scenarios. Three major architectural parameters of CNN were evaluated and compared in  [45]  to demonstrate their impact on the overall performance of the network. An optimal design of deep networks and the best performance of the network were achieved based on this comparison. The performance of CNN models was further enhanced by a new MSE-based ensemble approach, which was used for regression problems.
A bagging method was utilized to demonstrate the superior performance of this new approach. A vision-based lateral control approach for autonomous driving using reinforcement learning and deep learning methods was proposed in  [46]. The approach consists of a perception module and a control module. The perception module takes a driver-view image as input and predicts track features using a neural network of multi-task learning. The control module generates control decisions based on these features using reinforcement learning. The approach was evaluated using Visual TORCS (VTORCS) and a deep reinforcement learning environment based on the open racing car simulator (TORCS) to improve data efficiency. The trained reinforcement learning controller outperformed the model predictive control (MPC) and linear quadratic regulator (LQR) controllers on various tracks.
Further, a switching control based on higher-order sliding mode (HOSM) was developed to compensate the external disturbances effect. The simulation results demonstrate the effectiveness of the proposed control in terms of lateral stability and lane keeping in the high-fidelity environment of Carsim-Matlab Simulink in diverse road and environmental conditions. In  [52] , a speed-adaptive model-free lateral control strategy was developed with a wide range of operation in order to increase the situations where the autonomous vehicles steer without intervention. A minimum level of comfort and safety were considered. A systematic procedure was used to test the performance of controllers on trajectories with different dynamic constraints and shapes. The simulation and real-world tests explained that the speed-adaptive model-free Control (MFC) has a superior performance compared to other controllers with the same structure, such as a MFC and a PID.
Despite model uncertainties, the proposed control demonstrated superior performance compared to a linear-state feedback control in MATLAB/Simulink. This controller is suitable for practical implementation, as it only requires a sensor in addition to vehicle velocity to measure the relative distance between the lead and controlled vehicle. In  [61] , a longitudinal control algorithm was developed for an autonomous vehicle without the need for vehicle parameter identification. The modified MRAC approach was studied with appropriate initial conditions, ignoring powertrain dynamics and road slope changes. Despite these simplifications, the adaptive algorithm effectively tracks speed profiles with comfortable acceleration and robustness to environmental variations. The control architecture is suitable for vehicles with approximately known parameters.
In  [67] , a sensorless state estimation approach was presented for brake pressure in vehicles using deep learning, with the goal of achieving high autonomy and safe driving. The structured deep neural network (DNN) was trained using techniques such as rectified and dropout units, yielding a highly accurate model for brake-pressure state estimation. Training and validation were performed using experimental data collected from a real test vehicle connected to a chassis dynamometer. The effectiveness and applicability of the proposed technique were demonstrated, with a root mean square error (RMSE) of 0. 048 MPa achieved for brakepressure state estimation. In  [68] , an RL model was introduced for optimizing car-following performance by integrating comfort, efficiency, and safety factors. The RL agent learned vehicle speed control through simulations using a reward function derived from human driving data.
The trajectory prediction was performed using a deep learning model based on long short-term memory (LSTM), incorporating the historical relationship between a target vehicle and lanes. The interaction among adjacent vehicles was captured through a graph convolutional network (GCN) with self-attention. The prediction model utilizes sensor data locally acquired from AVs. The determination of acceleration inputs is done by model predictive control (MPC), with a focus on prioritizing safety and ride quality. Comparative studies showed improved accuracy compared to baseline approaches, and automated driving tests confirmed the safety and comfort achieved by the proposed control algorithm with the LSTM-based prediction model. A safe velocity control method for autonomous vehicles (AVs) was proposed in [72], considering the following vehicle in car-following models.
Simulations validated the approach, demonstrating efficient path tracking, acceptable lateral errors, and accurate longitudinal speed tracking. Overcoming the challenge of strong coupling between lateral and longitudinal dynamics in the design of robust, real-time controllers for real vehicle implementation remains a key consideration. In  [80] , two coupled controllers were proposed: one utilizing Lyapunov control techniques and the other employing immersion and invariance with a sliding mode approach. These controllers guarantee robust tracking of the reference trajectory and desired speed while considering the strong coupling between the vehicle's lateral and longitudinal dynamics. Validation was conducted via simulation in Matlab/Simulink and experimental tests using a robotized vehicle (Renault-ZOE).
The proposed approach by  [81]  for autonomous driving involves coordinated longitudinal and lateral control, prioritizing stability and comfort. It introduced an particle swarm optimized proportional-integral-derivative (PSO-PID) method for speed tracking and an improved linear parameter varying model predictive controller (LPV-MPC) for lateral dynamics control. The LPV-MPC incorporates an adaptive LPV model and an enhanced cost function to enhance performance and stability. Matlab/Carsim co-simulations validated the controllers for general trajectory tracking and double-lane-change scenarios, demonstrating reasonable performance and robustness against wind disturbances. A control framework that improves lateral stability and trajectory tracking accuracy by jointly estimating multiple parameters using an adaptive unscented Kalman filter was developed in  [82].
Stability conditions were formulated using a Lyapunov candidate function and BMIs. String stability and robust platoon control were ensured through additional conditions. Constraints such as actuator saturation and limited controller information were taken into account. Vehicle lateral control was achieved using a multi-model fuzzy controller, which maintains road position. The design conditions were expressed as LMIs solvable with numerical solvers. The effectiveness of the proposed control method was validated using the CarSim software package. The parameters need to be estimated, and the control structure is not simple.
To address this, a multi-modal multitask network was used, which incorporated both visual recordings and previous feedback speeds as inputs to predict steering angles and speed values. The proposed approach was evaluated on a newly collected SAIC dataset and a public Udacity dataset, and the results demonstrated the accuracy of speed value and steering angle predictions. The problem of error accumulation was addressed using methods of failure data synthesis in real road tests. An investigation of the effect of connected and autonomous vehicles (CAVs) on traffic flow in mixed traffic scenarios, with a focus on on-ramp merging and off-ramp diverging of vehicles, was presented in  [85]. The study explored the impact of increasing marketpenetration rates of CAVs and proposed a lane-changing cooperative strategy based on reinforcement learning to enable CAVs to make farsighted lane changes and improve traffic efficiency.
Previous research established that children with poorer taxonomic knowledge in the food domain display increased levels of food rejection. However, the food domain heavily lends itself to script and thematic conceptual knowledge (e. g. , pancakes at breakfast), to which young children already attend. This series of studies investigated the development of conceptual knowledge, specifically in the food domain, and the link with food rejection. Study 1 used a nonconflicting triad task testing children's knowledge of four subtypes of script and thematic associations (food-food pairs, food-utensil pairs, event scripts, and meal scripts) with children aged 3-6 years living in the United States (18 males and 14 females). Study 2 employed the same design along with a measure of food rejection in 3-to 6-year-olds living in France (67 males and 62 females).
Take, for example, knowing that eggs can be served at breakfast with toast. If children do not have sufficient knowledge of foods belonging to scripts (i. e. , eggs for breakfast) and thematically associated foods (i. e. , eggs with slices of toast), we predict that they will be left feeling uncertain about the appropriateness of the food scene. When an item or a situation holds a potential risk, it is safer to reject such an instance. This is especially true in a domain such as food in which ingesting a novel substance may lead to disgust, illness, or even toxicity  (Crane et al. , 2020). Therefore, our main prediction was that a lack of thematic and script knowledge of food was expected to be related to increased food rejection. However, the relation between food rejection and conceptual development is far from simple because conceptual knowledge is heavily dependent on lived experience  (Contento, 1981).
Evidence shows that food-to-mealtime scripts (e. g. , oatmeal at breakfast) form, through frequent consumption of food at a specific mealtime in the past or through the perceived appropriateness of consuming food at a given mealtime, learned implicitly through social cues  (McLeod et al. , 2020). Considering that a common disposition of picky eating and food neophobia is this rejection of food across eating periods and contexts, the opportunity to learn about script and thematic relations is mired and the cycle of food rejection is perpetuated. Therefore, it was equally expected that food rejection tendencies would hinder children's potential to experience and learn about conceptual knowledge in the food domain. Both directional theories support the idea that poor conceptual knowledge of food will be significantly negatively correlated with increased levels of food rejection in young children.
The three studies presented in this article focused on the developmental acquisition of thematic and script knowledge of foods and how conceptual development is linked with food rejection tendencies in children aged 3-7 years. Study 1 investigated the distinct developmental trajectories of thematic and script knowledge structures in the food domain. Study 2 expanded to investigate whether specific conceptual knowledge is linked to food rejection tendencies. The third and final study investigated thematic and script knowledge in a conflicting triad design that demanded the most appropriate relation in response to the situational demands. All three studies were preregistered, and the protocols, stimuli, anonymized datasets, and statistical scripts can be accessed at OSF project. All three studies received institutional review board (IRB) approval from the Stanford IRB and the Lyon II IRB.
g. , cereal-toast ''breakfast foods"). These associates are referred to as meal scripts. The first study applied a match-to-sample task to determine whether the heterogeneous script and thematic relations outlined above show distinct developmental trajectories. The preregistered hypothesis for the first study was that children's knowledge in the food domain would be predicted by association type and age. In  Lucariello et al. 's (1992)  qualitative investigation, both 4-and 7year-olds evoked script relations when establishing matches in several domains (food, clothing, animals, furniture, and tools), but thematic associates remained the most prevalent response in both age groups. Consequently, it was expected that food-food and food-utensil associates would develop earlier than event and meal script concepts in children aged 3-6 years.
Design and materials Study 1 used a forced-choice triad task, pitting a match against a distractor, with six trials for each of the respective conceptual relations outlined above: (A) event scripts (e. g. , cake-celebration), (B) food-food (e. g. , wafer cone-ice cream), (C) food-utensils (e. g. , soup-spoon), and (D) meal scripts (e. g. , bread-cereal). Each child was consecutively tested across all four association types, and an example triad always preceded each association to explain the association sought. To determine possible order effects, half the participants received the trials in one order (Association Types A-B-C-D), and the remaining half received the trials in the reverse order (Association Types D-C-B-A) (see Fig. 1 ). To determine whether a child showed a preference for specific stimuli, the distractor for one triad was a correct match for another triad.
Statistical analyses Data sets for the three studies and the respective scripts are openly available in the OSF directory. The significance level was set at p <. 05. Age was calculated in months and then coded as a binary variable, with children aged 34-48 months coded as younger and those aged 54-68 months coded as older. For each trial, a 1 was assigned to children's selection of the associated choice and a 0 was assigned to children's selection of the distractor choice; a composite score was calculated for each association type. Descriptive statistics were run for all variables, and independent-samples t testing was conducted to determine whether children performed above chance level (M >. 50) in each association. For hypothesis testing, mixed-model regression models were used to determine how age (independent variable) affects conceptual knowledge (dependent variable).
269, p =. 001) have a significant effect on children's performance in the thematic and script categorization tasks. There were no significant random or interaction effects. The older children (n = 16) scored significantly better than the younger children (n = 16) across all associations and total performance (see Table  1 ). The estimated increase in task performance for 4. 5to 5. 5-year-olds compared with 3-and 4-year-olds was 0. 219 (95% confidence interval = [0. 130, 0. 307]). Post hoc analyses of the main effects of association type show that scores differed significantly for the food-food (b =. 12, p =. 003) and food-utensils (b =. 156, p <. 001) associates compared with the meal scripts. Scores for event scripts were not significantly better than scores for meal scripts (see Table  2  for estimates).
Discussion In line with the literature, there was a significant yet steady development in the proficiency of all four conceptual relations from 3 to 6 years of age  (Nguyen & Murphy, 2003). As the first study to directly compare the development of distinct conceptual relations, the results demonstrate that the heterogeneous nature of thematic relations renders them developmentally different. The main effects of association type show that the script and thematic subtypes (meal scripts, food-food, and food-utensils) differ significantly. For children aged 34 to 47 months, knowledge of functional relations and food-food relations was significantly better than knowledge of meal script relations. Furthermore, knowledge of meal scripts was at chance level for the younger children. From 55 to 68 months of age, there were no significant differences in knowledge across the four conceptual relations tested, demonstrating that children appear to have relatively proficient knowledge of all four forms of conceptual relations in the food domain.
Confirmatory hypothesis testing As witnessed in Study 1, the model of best fit included fixed effects of age and association type; there were no significant random or interaction effects. The tests of fixed effects show that age (F = 88. 574, p <. 001) and association type (F = 21. 736, p <. 001) each have a significant effect on children's performance. As witnessed with the U. S. participants, scores for food-food and food-utensil concepts were significantly better than scores for meal scripts within participants (b =. 123, p <. 001, and b =. 156, p <. 001, respectively). Scores for event scripts were not significantly better than scores for meal scripts (see Table  3 ). Food pickiness and food neophobia did not improve the fit of the model and were omitted from the model predicting performance.
Discussion In conjunction with the results from Study 1, Study 2 showed that both association type and age were predictive of knowledge of conceptual relations in the food domain. Again, there were significant differences in children's performance between scripts and thematic associates. Scores for event and meal scripts were significantly lower than scores for food-food and food-utensil associates within participants. This speaks in favor that although the material was recalibrated to a different sample, the design and stimuli sets provide robust cross-cultural findings. Food pickiness and food neophobia did not improve the fit of the model predicting performance on the food knowledge task. Equally, there was a lack of significant interaction terms in the best fitting model, indicating that children's food rejection was not associated with specific trial types.
This result seems surprising considering the previous research by  Pickard et al. (2021) , who concluded that increased levels of food rejection were linked to poorer thematic knowledge in the food domain. The results of the current experiment call for reinterpreting the results of Pickard et al. , who used an analogical reasoning task in which children were required to pick the most appropriate response from a taxonomic or thematic match. Children with higher food rejection were perhaps capable of identifying both thematic and taxonomic relations but could not select the most appropriate in line with the task demands. This line of reasoning suggests that children with higher food rejection can identify common food associates and scripts when there is no strong contender, as shown by the high percentage of correct answers.
Participants The previous analogy task incorporating thematic knowledge and food rejection detected an effect size of 247  (Pickard et al. , 2021) , which would require 126 children to obtain 80 power at the standard 05 alpha error probability. Given that the task design for Study 3 was less demanding than the analogy task by  Pickard et al. (2021) , a larger effect was expected, requiring a smaller sample size. Children were recruited from a preschool different from that in Study 2 in a middle socioeconomic area of France. National ethical regulations do not permit the collection of individual demographic data collection. In total, 39 girls and 33 boys aged 48. 95 to 84. 04 months, with a mean age of 66 months (SD = 10. 9), provided oral assent to participate in the task. Of this sample, 34 children completed the script associations followed by the thematic associations, and the remaining 38 children completed the thematic associations and then the script associations.
Procedure The asagneolers were tested individually in a quiet area of the school for approximately 10 min. The researchers introduced two novel characters, Feppe and Cronus, and explained that they were from a faraway planet. Therefore, they needed the children's help to understand objects and foods normally found in specific situations. Children completed 24 test trials in one of two counterbalanced sequences. Four non-food training trials were used at the beginning of both scenarios to explain the task to the children and determine whether they had understood the need to identify an associate or a replacement (see Fig. 3 ). The data for children who did not correctly respond to 75% or more of the training trials were excluded from the analyses. The script scenario was framed in that the character had a specific meal, and because there was no more of one food left, he needed to select a suitable substitute for that meal script (''Feppe was having lunch, but there was no fish left.