Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

The random nature of traffic conditions on freeways can cause excessive congestions and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use machine learning approaches to develop a novel real-time prediction model for ramp metering. We evaluate the potentials of our approach in providing promising results by comparing it with a baseline traffic-responsive ramp metering algorithm.Comment: 5 pages, 11 figures, 2 table

Computational Intelligence in Highway Management: A Review

Highway management systems are used to improve safety and driving comfort on highways by using control strategies and providing information and warnings to drivers. They use several strategies starting from speed and lane management, through incident detection and warning systems, ramp metering, weather information up to, for example, informing drivers about alternative roads. This paper provides a review of the existing approaches to highway management systems, particularly speed harmonization and ramp metering. It is focused only on modern and advanced approaches, such as soft computing, multi-agent methods and their interconnection. Its objective is to provide guidance in the wide field of highway management and to point out the most relevant recent activities which demonstrate that development in the field of highway management is still important and that the existing research exhibits potential for further enhancement

AN INTEGRATED CONTROL MODEL FOR FREEWAY INTERCHANGES

This dissertation proposes an integrated control framework to deal with traffic congestion at freeway interchanges. In the neighborhood of freeway interchanges, there are six potential problems that could cause severe congestion, namely lane-blockage, link-blockage, green time starvation, on-ramp queue spillback to the upstream arterial, off-ramp queue spillback to the upstream freeway segments, and freeway mainline queue spillback to the upstream interchange. The congestion problem around freeway interchanges cannot be solved separately either on the freeways or on the arterials side. To eliminate this congestion, we should balance the delays of freeways and arterials and improve the overall system performance instead of individual subsystem performance. This dissertation proposes an integrated framework which handles interchange congestion according to its severity level with different models. These models can generate effective control strategies to achieve near optimal system performance by balancing the freeway and arterial delays. The following key contributions were made in this dissertation: 1. Formulated the lane-blockage problem between the movements of an arterial intersection approach as an linear program with the proposed sub-cell concept, and proposed an arterial signal optimization model under oversaturated traffic conditions; 2. Formulated the traffic dynamics of a freeway segment with cell-transmission concept, while considering the exit queue effects on its neighboring through lane traffic with the proposed capacity model, which is able to take the lateral friction into account; 3. Developed an integrated control model for multiple freeway interchanges, which can capture the off-ramp spillback, freeway mainline spillback, and arterial lane and link blockage simultaneously; 4. Explored the effectiveness of different solution algorithms (GA, SA, and SA-GA) for the proposed integrated control models, and conducted a statistical goodness check for the proposed algorithms, which has demonstrated the advantages of the proposed model; 5. Conducted intensive numerical experiments for the proposed control models, and compared the performance of the optimized signal timings from the proposed models with those from Transyt-7F by CORSIM simulations. These comparisons have demonstrated the advantages of the proposed models, especially under oversaturated traffic conditions

New Framework and Decision Support Tool to Warrant Detour Operations During Freeway Corridor Incident Management

As reported in the literature, the mobility and reliability of the highway systems in the United States have been significantly undermined by traffic delays on freeway corridors due to non-recurrent traffic congestion. Many of those delays are caused by the reduced capacity and overwhelming demand on critical metropolitan corridors coupled with long incident durations. In most scenarios, if proper detour strategies could be implemented in time, motorists could circumvent the congested segments by detouring through parallel arterials, which will significantly improve the mobility of all vehicles in the corridor system. Nevertheless, prior to implementation of any detour strategy, traffic managers need a set of well-justified warrants, as implementing detour operations usually demand substantial amount of resources and manpower. To contend with the aforementioned issues, this study is focused on developing a new multi-criteria framework along with an advanced and computation-friendly tool for traffic managers to decide whether or not and when to implement corridor detour operations. The expected contributions of this study are: * Proposing a well-calibrated corridor simulation network and a comprehensive set of experimental scenarios to take into account many potential affecting factors on traffic manager\u27s decision making process and ensure the effectiveness of the proposed detour warrant tool; * Developing detour decision models, including a two-choice model and a multi-choice model, based on generated optima detour traffic flow rates for each scenario from a diversion control model to allow responsible traffic managers to make best detour decisions during real-time incident management; and * Estimating the resulting benefits for comparison with the operational costs using the output from the diversion control model to further validate the developed detour decision model from the overall societal perspective

강화학습을 활용한 고속도로 가변제한속도 및 램프미터링 전략 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 김동규.Recently, to resolve societal problems caused by traffic congestion, traffic control strategies have been developed to operate freeways efficiently. The representative strategies to effectively manage freeway flow are variable speed limit (VSL) control and the coordinated ramp metering (RM) strategy. This paper aims to develop a dynamic VSL and RM control algorithm to obtain efficient traffic flow on freeways using deep reinforcement learning (DRL). The traffic control strategies applying the deep deterministic policy gradient (DDPG) algorithm are tested through traffic simulation in the freeway section with multiple VSL and RM controls. The results show that implementing the strategy alleviates the congestion in the on-ramp section and shifts to the overall sections. For most cases, the VSL or RM strategy improves the overall flow rates by reducing the density and improving the average speed of the vehicles. However, VSL or RM control may not be appropriate, particularly at the high level of traffic flow. It is required to introduce the selective application of the integrated control strategies according to the level of traffic flow. It is found that the integrated strategy can be used when including the relationship between each state detector in multiple VSL sections and lanes by applying the adjacency matrix in the neural network layer. The result of this study implies the effectiveness of DRL-based VSL and the RM strategy and the importance of the spatial correlation between the state detectors.최근에는 교통혼잡으로 인한 사회적 문제를 해결하기 위해 고속도로를 효율적으로 운영하기 위한 교통통제 전략이 다양하게 개발되고 있다. 고속도로 교통류를 효과적으로 관리하기 위한 대표적인 전략으로는 차로별 제한속도를 다르게 적용하는 가변 속도 제한(VSL) 제어와 진입 램프에서 신호를 통해 차량을 통제하는 램프 미터링(RM) 전략 등이 있다. 본 연구의 목표는 심층 강화 학습(deep reinforcement learning)을 활용하여 고속도로의 효율적인 교통 흐름을 얻기 위해 동적 VSL 및 RM 제어 알고리즘을 개발하는 것이다. 고속도로의 여러 VSL과 RM 구간에서 시뮬레이션을 통해 심층 강화학습 알고리즘 중 하나인 deep deterministic policy gradient (DDPG) 알고리즘을 적용한 교통류 제어 전략을 검증한다. 실험 결과, 강화학습 기반 VSL 또는 RM 전략을 적용하는 것이 램프 진입로 구간의 혼잡을 완화하고 나아가 전체 구간의 혼잡을 줄이는 것으로 나타났다. 대부분의 경우 VSL이나 RM 전략은 본선과 진입로 구간의 밀도를 줄이고 차량의 평균 통행 속도를 증가시켜 전체 교통 흐름을 향상시킨다. VSL 또는 RM 전략들은 높은 수준의 교통류에서 적절하지 않을 수 있어 교통류 수준에 따른 전략의 선택적 도입이 필요하다. 또한 검지기간 지리적 거리와 관련한 인접 행렬을 포함하는 graph neural network layer이 여러 지점 검지기의 공간적 상관 관계를 감지하는 데 이용될 수 있다. 본 연구의 결과는 강화학습 기반 VSL과 RM 전략 도입의 필요성과 지점 검지기 간의 공간적 상관관계의 중요성을 반영하는 전략 도입의 효과를 시사한다.Chapter 1. Introduction 1 Chapter 2. Literature Review 4 Chapter 3. Methods 8 3.1. Study Area and the Collection of Data 8 3.2. Simulation Framework 11 3.3. Trip Generation and Route Choice 13 3.4. Deep Deterministic Policy Gradient (DDPG) Algorithm 14 3.5. Graph Convolution Network (GCN) Layer 17 3.6. RL Formulation 18 Chapter 4. Results 20 4.1. VSL and RM 20 4.2. Efficiency according to the flow rate 28 4.3. Effectiveness of the GCN Layer 33 Chapter 5. Conclusion 34 Bibliography 37 Abstract in Korean 44석