Driver's Trajectory Research Articles

Sustainable impact analysis of freight pooling strategies on city crowdsourcing logistics platform

This study investigates the effects of freight pooling strategies on urban crowdsourcing logistics, focusing on economic, social, and environmental outcomes. Utilising a mixed-integer linear programming model with an adaptive large neighbourhood search algorithm, our goal is to optimise the cost-efficiency of the freight pooling system. Real-world delivery and driver trajectory data from a major Chinese crowdsourcing logistics platform, along with high-resolution vehicle telematics data, validate our model in five scenarios, each defined by distinct cost coefficients reflecting diverse stakeholder priorities. Results show potential for up to a 21.3% reduction in carbon emissions, a 28.3% decrease in truck activity spatial coverage, and a 7.6% increase in available drivers. However, deadheading trips, due to order consolidation into fewer vehicles without an increase in overall demand, could offset maximum carbon reduction benefits by 16.4%. Other effects on customers’ and drivers’ welfare are explored for a comprehensive quantitative assessment of freight pooling strategies’ sustainability benefits.

Error accumulation when steering toward curves.

To steer a vehicle, humans must process incoming signals that provide information about their movement through the world. These signals are used to inform motor control responses that are appropriately timed and of the correct magnitude. However, the perceptual mechanisms determining how drivers process visual information remain unclear. Previous research has demonstrated that when steering toward a straight road-line, drivers accumulate perceptual evidence (error) over time to initiate steering action (Accumulator framework), rather than waiting for perceptual evidence to surpass time-independent fixed thresholds (Threshold framework). The more general case of steering around bends (with a requirement that the trajectory is adjusted to match the road curvature ahead) provides richer continuously varying information. The current experiment aims to establish whether the Accumulator framework provides a good description of human responses when steering toward curved road-lines. Using a computer-generated steering correction paradigm, drivers (N = 11) steered toward intermittently appearing curved road-lines that varied in position and radius with respect to the driver's trajectory. The Threshold framework predicted that steering responses would be of fixed magnitude and at fixed absolute errors across conditions regardless of the rate of error development. Conversely, the Accumulator framework predicted that drivers should respond to larger absolute errors when the error signal developed at a faster rate. Results were consistent with an Accumulator framework in a manner that supports previous investigations and the computational modeling literature. We propose that the accumulation of perceptual evidence captures human behavior in a variety of steering contexts that drivers face in the real world. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An Active Gate Driver of SiC MOSFET Module Based on PCB Rogowski Coil for Optimizing Tradeoff Between Overshoot and Switching Loss

The superior characteristics of the silicon carbide metal-oxide-semiconductor field-effect transistor (SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> ) allow its wide use for improving the efficiency and power density of power electronic systems. However, the higher switching speed exacerbates the problems of overshoot, oscillation, and electromagnetic interference (EMI), which need to be properly addressed. In this article, a novel stage-detection closed-loop active gate driver (AGD) based on the printed-circuit-board (PCB) Rogowski Coil is proposed for optimizing the switching performance of SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s. And its stage identification threshold design can weaken the influence of the varying nonlinear parameters, especially for the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> process. First, the gate driver trajectory and the switching process of the SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> are analyzed. The optimal driver parameter dynamic configuration among the existing stage-control schemes is defined and unified, which aims to optimize the tradeoff between the overshoot and switching loss. Then, the parameter design of the PCB Rogowski Coil is illustrated. And the operation principle and working process of the proposed AGD are introduced. Finally, the performance of the proposed AGD and the effectiveness of the stage-control schemes are verified in the double-pulse test under different conditions. The experimental results show that the proposed AGD can not only reduce the overshoot and suppress the oscillation but also optimize the compromise of the switching loss and switching time.

Adaptive Steering Torque Coupling Framework Considering Conflict Resolution for Human-Machine Shared Driving

Human-machine shared control has become an effective cutting-edge approach to enhance driving safety and assist in the transition from manual to autonomous driving. However, driving authority allocation when conflict occurs between a human driver and a machine still represents an intractable problem. In order to solve this problem, this paper proposes an adaptive steering torque coupling framework to achieve the human-machine shared control with conflict resolution. Additionally, a driver trajectory prediction method considering human-machine interaction is established based on the extended state observer and long short-term memory network. In addition, the human-machine shared steering system is modeled based on a non-cooperative dynamic game by the prediction model method for path-tracking, and the obtained solution is given according to the Nash equilibrium. Particularly, a dynamic load allocation approach is designed to resolve human-machine conflict and relax the driver. In order to verify the strategy formed based on the proposed framework, a driver-in-the-loop experiment is conducted. The experimental results reveal that the proposed strategy can effectively reduce the human-machine conflict torque and ensure a driver has absolute control authority. Furthermore, the proposed strategy can also transfer the driving workload between the driver and machine, provide driving experience, or improve driving comfort when there is no conflict.

CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy

Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this letter, we propose a novel multi-task intent recognition neural network that predicts not only probabilistic driver trajectories, but also utility statistics associated with the predictions for a given downstream task. We establish a decision criterion for parallel autonomy that takes into account the role of driver trajectory prediction in real-time decision making by reasoning about estimated task-specific utility statistics. We further improve the robustness of our system by considering uncertainties in downstream planning tasks that may lead to unsafe decisions. We test our online system on a realistic urban driving dataset, and demonstrate its advantage in terms of recall and fall-out metrics compared to baseline methods, and demonstrate its effectiveness in intervention and warning use cases.

Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach

After first extending Newell’s car-following model to incorporate time-dependent parameters, this paper describes the Dynamic Time Warping (DTW) algorithm and its application for calibrating this microscopic simulation model by synthesizing driver trajectory data. Using the unique capabilities of the DTW algorithm, this paper attempts to examine driver heterogeneity in car-following behavior, as well as the driver’s heterogeneous situation-dependent behavior within a trip, based on the calibrated time-varying response times and critical jam spacing. The standard DTW algorithm is enhanced to address a number of estimation challenges in this specific application, and a numerical experiment is presented with vehicle trajectory data extracted from the Next Generation Simulation (NGSIM) project for demonstration purposes. The DTW algorithm is shown to be a reasonable method for processing large vehicle trajectory datasets, but requires significant data reduction to produce reasonable results when working with high resolution vehicle trajectory data. Additionally, singularities present an interesting match solution set to potentially help identify changing driver behavior; however, they must be avoided to reduce analysis complexity.

Diverging Driver Performance along Deceleration Lanes: Driving Simulator Study

Highway diverge areas are often characterized by high crash rates. Worldwide, geometric design guidelines propose deceleration lanes with a parallel or tapered layout to promote a free-vehicle condition, while the guidelines neglect the potential effects of traffic flow and risky driving behaviors that are induced by the wrong perceptions of road geometry and vehicle interference. A driving simulation study was conducted to analyze the effects of traffic flow and deceleration lane geometry on the driving performance of diverging drivers. Two types of deceleration lanes (parallel and tapered) were implemented in a simulator, and two traffic conditions (low and high traffic flows) were simulated for each type of lane. Thirty-one drivers took part in the experiments. The effects of traffic flow on driving performance while drivers approached the diverge area and during their deceleration were investigated. The study found that lane type significantly affected the speeds of diverging drivers, independent of traffic conditions, with greater interference with the through traffic on the tapered lane. Traffic conditions were found to influence the driver's trajectory along the tapered lane, and this trajectory delayed the exiting maneuver under heavy traffic conditions. This effect was not found to have been significant for parallel deceleration lanes on which the deceleration rates were significantly affected by traffic conditions: higher decelerations were recorded under light traffic conditions. No significant traffic effects on deceleration rates were found on the tapered lane.

An indoor locationtracking system for smart parking

There are always frustrations for drivers in finding parking space and parking is costly in almost every major city in the world. In this paper, we propose a crowdsourcing solution by exploiting sensors in the smartphone to collect real-time parking availability information. In particular, we utilise a pedestrian dead reckoning (PDR) system to track the driver's trajectory to detect when he is about to leave his parking space. However, the effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we implement a waist-mounted-based PDR method on a smartphone that can measure the user's moving distance with a high accuracy. Furthermore, we design a map-matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.