Robust classification of city roadway objects for traffic related applications

The increasing prevalence of video data, particularly from traffic and surveillance cameras, is accompanied by a growing need for improved object detection, tracking, and classification techniques. In order to encourage development in this area, the AI City Challenge, sponsored by IEEE Smart World and NVIDIA, cultivated a competitive environment in which teams from all over the world sought to demonstrate the effectiveness of their models after training and testing on a common dataset of 114,766 unique traffic camera keyframes. Models were constructed for two distinct purposes; track 1 designs addressed object detection, localization and classification, while track 2 designs aimed to produce novel approaches towards traffic related application development. Careful tuning of the Darknet framework\u27s YOLO (You Only Look Once) architecture allowed us to achieve 2nd place scores in track 1 of the competition. Our model was able to achieve inference beyond 50 frames per second (FPS) when performing on the NVIDIA DGX-1\u27s Tesla P100 GPU and up to 37 FPS on a NVIDIA GTX 1070 GPU. However, the NVIDIA Jetson TX2 edge device had a lackluster 2 FPS inference speed. To produce truly competitive automated traffic control systems, either more preferment edge device hardware or revolutionary neural network architectures are required. While our track 2 model approach demonstrated that it is reasonable to obtain useful traffic related metrics without the use of the region proposal networks and classification methods utilized in other models typically associated with traffic control systems

Developing a Driver-Centric Roadway Classification System with Multidimensional Scaling

Various systems exist to classify roadway environments; however most do not consider driver-relevant perceptual components. A perceptually based roadway classification system has the potential to support the placement of signage (or removal of extraneous clutter) in the right-of-way as a means to enhance driver performance. The present study sought to determine which environmental factors are attended to by roadway users. Thirteen participants first rated the similarity of 14 roadway environments and then rated each environment on 5 different descriptors (built-up, clutter, openness, aesthetically pleasing, organized/predictable). The resultant data were analyzed using a methodology rarely taken advantage of in the field of transportation: Multidimensional Scaling (MDS). MDS revealed the participants relied on two primary dimensions when rating the similarity of the roadway environments. These two dimensions related closely with: 1) organization/predictability and 2) clutter and aesthetics. This methodology provides a simple way to gain access to drivers’ perceptions of the roadway environment and appears to be a promising first step toward developing a user-focused roadway classification system

Advanced traffic video analytics for robust traffic accident detection

Automatic traffic accident detection is an important task in traffic video analysis due to its key applications in developing intelligent transportation systems. Reducing the time delay between the occurrence of an accident and the dispatch of the first responders to the scene may help lower the mortality rate and save lives. Since 1980, many approaches have been presented for the automatic detection of incidents in traffic videos. In this dissertation, some challenging problems for accident detection in traffic videos are discussed and a new framework is presented in order to automatically detect single-vehicle and intersection traffic accidents in real-time. First, a new foreground detection method is applied in order to detect the moving vehicles and subtract the ever-changing background in the traffic video frames captured by static or non-stationary cameras. For the traffic videos captured during day-time, the cast shadows degrade the performance of the foreground detection and road segmentation. A novel cast shadow detection method is therefore presented to detect and remove the shadows cast by moving vehicles and also the shadows cast by static objects on the road. Second, a new method is presented to detect the region of interest (ROI), which applies the location of the moving vehicles and the initial road samples and extracts the discriminating features to segment the road region. After detecting the ROI, the moving direction of the traffic is estimated based on the rationale that the crashed vehicles often make rapid change of direction. Lastly, single-vehicle traffic accidents and trajectory conflicts are detected using the first-order logic decision-making system. The experimental results using publicly available videos and a dataset provided by the New Jersey Department of Transportation (NJDOT) demonstrate the feasibility of the proposed methods. Additionally, the main challenges and future directions are discussed regarding (i) improving the performance of the foreground segmentation, (ii) reducing the computational complexity, and (iii) detecting other types of traffic accidents

Towards Transportation Digital Twin Systems for Traffic Safety and Mobility Applications: A Review

Digital twin (DT) systems aim to create virtual replicas of physical objects that are updated in real time with their physical counterparts and evolve alongside the physical assets throughout its lifecycle. Transportation systems are poised to significantly benefit from this new paradigm. In particular, DT technology can augment the capabilities of intelligent transportation systems. However, the development and deployment of networkwide transportation DT systems need to take into consideration the scale and dynamic nature of future connected and automated transportation systems. Motivated by the need of understanding the requirements and challenges involved in developing and implementing such systems, this paper proposes a hierarchical concept for a Transportation DT (TDT) system starting from individual transportation assets and building up to the entire networkwide TDT. A reference architecture is proposed for TDT systems that could be used as a guide in developing TDT systems at any scale within the presented hierarchical concept. In addition, several use cases are presented based upon the reference architecture which illustrate the utility of a TDT system from transportation safety, mobility and environmental applications perspective. This is followed by a review of current studies in the domain of TDT systems. Finally, the critical challenges and promising future research directions in TDT are discussed to overcome existing barriers to realize a safe and operationally efficient connected and automated transportation systems.Comment: 15 pages, 2 figures; corrected issue in author(s) fiel