Evaluation of Axle-Based and Length-Based Vehicle Classification Stations

2013 ◽  
Vol 2339 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Seoungbum Kim ◽  
Benjamin Coifman
Keyword(s):  
Classification Scheme ◽  
Ground Truth ◽  
Piezoelectric Sensor ◽  
Vehicle Classification ◽  
Passenger Vehicles ◽  
Loop Detectors ◽  
Length Range ◽  
Vehicle Fleet ◽  
Classification Evaluation ◽  
Almost All

This study evaluated the performance of three permanent vehicle classification stations on freeways against concurrent video-based ground truth. All stations had dual loop detectors and a piezoelectric sensor in each lane, which together provided axle-based and length-based classification. Evaluation was done at individual, per vehicle resolution for each vehicle that passed during the study periods (more than 18,000 vehicles in uncongested conditions). Although the stations exhibited good performance overall (97% correct), the performance for trucks was poor; for example, only 60% of the single-unit trucks (SUTs) were correctly classified. All observed errors were diagnosed. Some errors could be fixed quickly, and others could not. Data from one site were used to revise the classifier to solve almost all fixable errors, and then performance at another location was tested. A chronic error found in the research was intrinsic to the vehicle fleet and may be impossible to correct with existing sensors: the shorter SUTs have a length range and axle spacing range that overlap those of passenger vehicles. Depending on calibration, SUTs may be counted as passenger vehicles or vice versa. Such errors should be expected at most classification stations. All subsequent uses of the classification data must accommodate this unavoidable blurring error. Because of the blurring, the axle classification station cannot be uncritically used to calibrate the boundary between passenger vehicles and SUTs for length-based classification stations, because unavoidable errors in axle-based classification would be amplified in the length-based classification scheme.

Proton: Its rise, Fall, and Future Prospects

2012 ◽  
Vol 16 (02) ◽  
pp. 347-377
Author(s):  
Jane Terpstra Tong ◽  
Robert H. Terpstra ◽  
Ngat Chin Lim
Keyword(s):  
Joint Venture ◽  
Trade Policies ◽  
Business Decisions ◽  
Passenger Vehicles ◽  
Social Agenda ◽  
Hands On ◽  
Asean Countries ◽  
The Government ◽  
Almost All ◽  
Economic Development Model

This case focuses on the challenges faced by a Malaysian state-owned automobile manufacturer, Proton. In so doing, it exemplifies the political context in which businesses, both domestic and foreign, operate in Malaysia. What makes Proton unique is its origin as the brainchild of Tun Dr. Mahathir bin Mohammad, Malaysia's fourth Prime Minister. Mahathir was one of the longest-serving leaders in Asia when he resigned in 2003. Over his 22-year reign, Mahathir and his government made several fundamental changes to Malaysia's institutions and his legacy is still reflected in the current social, political and economic institutions. One of the more controversial economic programs he championed was the National Car Project, under which Proton was established. When Mahathir decided to industrialise Malaysia's economy, he did not look to the west for direction, but instead turned to the east — Japan. He adopted the Japanese economic development model that emphasises hands-on government involvement in the economy. To form Proton, he selected Japanese Mitsubishi Motors as the joint venture partner and within two years Proton was rolling out its own vehicles, which in effect were the “rebadged” version of Mitsubishi's Lancer. To ensure there were customers for Proton vehicles, the government raised import tariffs, making it very expensive to buy foreign imports. It also made Proton the official supplier for almost all government passenger vehicles. Under the protection policies of Mahathir, Proton grew to dominate the domestic market. However, it was unable to succeed in obtaining the desired technology from its Japanese partner, or in developing the ability to survive independently and compete effectively, especially in the international market. Part of Proton's weakness stemmed from its social agenda, which favoured bumiputera suppliers, even at the expense of cost and quality efficiency. Proton therefore serves as a good example to illustrate what can happen to a business when it is over-protected, and when business decisions are not made on merit-based principles. Proton's weaknesses were further exposed when the government allowed the establishment of a second national automaker, Perodua, in 1993. The recent free-trade policies adopted by the ASEAN countries, and also by China and India, have put even more pressure on Proton to transform. But the question is how?

scholarly journals Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2001 ◽  
Author(s):  
Eugin Hyun ◽  
YoungSeok Jin
Keyword(s):  
Classification Scheme ◽  
Vehicle Classification ◽  
Doppler Spectrum ◽  
Support Vector ◽  
Radar Sensor ◽  
Moving Vehicle ◽  
Fmcw Radar ◽  
Wave Radar ◽  
Spectrum Feature ◽  
Feature Based

In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.

Traffic State Estimation in Heterogeneous Networks with Stochastic Demand and Supply: Mixed Lagrangian–Eulerian Approach

2019 ◽  
Vol 2673 (11) ◽  
pp. 114-126 ◽  
Author(s):  
Mohammadreza Kavianipour ◽  
Ramin Saedi ◽  
Ali Zockaie ◽  
Meead Saberi
Keyword(s):  
State Estimation ◽  
Network Traffic ◽  
Ground Truth ◽  
Solution Algorithm ◽  
Estimation Problem ◽  
Demand And Supply ◽  
Traffic State ◽  
Probe Vehicles ◽  
Loop Detectors ◽  
Traffic State Estimation

A network fundamental diagram (NFD) represents the relationship between network-wide average flow and average density. Network traffic state estimation to observe NFD when congestion is heterogeneously distributed, as a result of a time-varying and asymmetric demand matrix, is a challenging problem. Recent studies have formulated the NFD estimation problem using both fixed measurements and probe trajectories. They are often based on a given ground-truth NFD for a single day demand. Stochastic variations in network demand and supply may significantly affect the approximation of an NFD. This study proposes a modified framework to estimate network traffic states to observe NFD while capturing the stochasticity in transportation networks. A mixed integer problem with non-linear constraints is formulated to address stochasticity in the NFD estimation problem. To solve this Nondeterministic Polynomial-hard (NP-hard) problem, a solution algorithm based on the simulated annealing method is applied. The problem is formulated and the solution algorithm is implemented to find an optimal configuration of loop detectors and probe vehicles to estimate the NFD of the Chicago downtown network and capture its day-to-day variations, considering a given available budget. Ground-truth NFDs and estimated NFDs based on a subset of loop detectors and probe vehicles are calculated using a simulation-based dynamic traffic assignment model, which is the best surrogate available to replicate real-world conditions. The main contribution of this study is to capture stochasticity in the demand and supply sides to find a more robust subset of links and trajectories to be acquired for the NFD estimation.

A Preview Steering Autopilot Control Algorithm for Four-Wheel-Steering Passenger Vehicles

1992 ◽  
Vol 114 (3) ◽  
pp. 401-408 ◽  
Author(s):  
A. Y. Lee
Keyword(s):  
Control Algorithm ◽  
Vision System ◽  
Control Law ◽  
Tracking Problem ◽  
Computer Vision System ◽  
Passenger Vehicles ◽  
Lane Tracking ◽  
Simulation Results ◽  
Almost All ◽  
Future Information

This paper addresses the control law design of a preview steering autopilot for a four-wheel-steering vehicle to perform automatic lane tracking. In the concept, an on-board computer vision system is used in lieu of the driver’s vision to track the roadway. The steering autopilot design is formulated as an optimal, discrete-time preview path tracking problem under the “perfect measurement” assumption. Simulation results indicate that the tracking performance of the steering autopilot was improved by preview relative to that calculated for an autopilot without preview. These results also indicate the existence of an effective preview time with which almost all the benefits of previewing future information can be obtained. This effective preview time is about three times the reciprocal of the autopilot’s bandwidth. Our study also indicates that preview steering autopilots can tolerate the use of actuators with a lower bandwidth than those designed without preview information.

scholarly journals TellTail: Fast Scoring and Detection of Dense Subgraphs

2020 ◽  
Vol 34 (04) ◽  
pp. 4150-4157
Author(s):  
Bryan Hooi ◽  
Kijung Shin ◽  
Hemank Lamba ◽  
Christos Faloutsos
Keyword(s):  
Search Algorithm ◽  
Ground Truth ◽  
Value Theory ◽  
Normal Variation ◽  
Dense Subgraph ◽  
Network Attacks ◽  
Dense Subgraphs ◽  
The Right ◽  
Almost All ◽  
Interesting Structure

Suppose you visit an e-commerce site, and see that 50 users each reviewed almost all of the same 500 products several times each: would you get suspicious? Similarly, given a Twitter follow graph, how can we design principled measures for identifying surprisingly dense subgraphs? Dense subgraphs often indicate interesting structure, such as network attacks in network traffic graphs. However, most existing dense subgraph measures either do not model normal variation, or model it using an Erdős-Renyi assumption - but this assumption has been discredited decades ago. What is the right assumption then? We propose a novel application of extreme value theory to the dense subgraph problem, which allows us to propose measures and algorithms which evaluate the surprisingness of a subgraph probabilistically, without requiring restrictive assumptions (e.g. Erdős-Renyi). We then improve the practicality of our approach by incorporating empirical observations about dense subgraph patterns in real graphs, and by proposing a fast pruning-based search algorithm. Our approach (a) provides theoretical guarantees of consistency, (b) scales quasi-linearly, and (c) outperforms baselines in synthetic and ground truth settings.

Vehicle Classification Model for Loop Detectors Using Neural Networks

2005 ◽  
Vol 1917 (1) ◽  
pp. 164-172 ◽  
Author(s):  
Yong-Kul Ki ◽  
Doo-Kwon Baik
Keyword(s):  
Neural Networks ◽  
Road Traffic ◽  
Recognition Rate ◽  
Classification Model ◽  
Vehicle Classification ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Loop Detectors ◽  
Variation Rate ◽  
The Neural Networks

Vehicle class is an important parameter in the process of road traffic measurement. Inductive loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve their accuracy, a new algorithm is suggested for ILD using backpropagation neural networks. In the developed algorithm, inputs to the neural networks are the variation rate of frequency and occupancy time. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.7%. Results verified that, compared with the conventional method based on ILD, the proposed algorithm improves the vehicle classification accuracy.

scholarly journals A Geometric Model for the Coiling of an Elastic Rod Deployed Onto a Moving Substrate

2015 ◽  
Vol 82 (12) ◽  
Author(s):  
Mohammad K. Jawed ◽  
Pierre-Thomas Brun ◽  
Pedro M. Reis
Keyword(s):  
Classification Scheme ◽  
Geometric Model ◽  
Contact Point ◽  
Elastic Rod ◽  
Sewing Machine ◽  
Physical Insight ◽  
Comprehensive Classification ◽  
Almost All ◽  
Coiling Process ◽  
Moving Substrate

We report results from a systematic numerical investigation of the nonlinear patterns that emerge when a slender elastic rod is deployed onto a moving substrate; a system also known as the elastic sewing machine (ESM). The discrete elastic rods (DER) method is employed to quantitatively characterize the coiling patterns, and a comprehensive classification scheme is introduced based on their Fourier spectrum. Our analysis yields physical insight on both the length scales excited by the ESM, as well as the morphology of the patterns. The coiling process is then rationalized using a reduced geometric model (GM) for the evolution of the position and orientation of the contact point between the rod and the belt, as well as the curvature of the rod near contact. This geometric description reproduces almost all of the coiling patterns of the ESM and allows us to establish a unifying bridge between our elastic problem and the analogous patterns obtained when depositing a viscous thread onto a moving surface; a well-known system known as the fluid-mechanical sewing machine (FMSM).

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