Minimize the Fuel Consumption of Connected Vehicles Between Two Red-Signalized Intersections in Urban Traffic

Lin, Qingfeng; Li, Shengbo Eben; Du, Xuejin; Zhang, Xiaowu; Peng, Huei; Luo, Yan; Li, Keqiang

doi:10.1109/tvt.2018.2864616

Cited by 59 publications

(36 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These states are transmitted to the neural network, and the network would make a response by updating the weight and bias. Additionally, considering that the current signal is also an important factor on the decision, we add the current signal phase as the third part of status representation in the system, denoted as S (3) t . S (3) t is a vector representation encoded with one-hot encoding [30] for current signal phase, which is encoded from 8 non-conflict signal phases shown as actions in Table 2.…”

Section: System Modeling For Rep-drqn 1) State Representationmentioning

confidence: 99%

“…Intersection, as a key node in the urban traffic network, plays a pivotal role in traffic manage and its optimization. Growing attention in academic and industrial fields has been paid to the traffic light control at intersection [3]. However, the limitation of road resources and the complexity of dynamical traffic flow make it hard to optimize signal timing to improve the capacity at intersection while keeping…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

Liao

Liu

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

The conflict between limited road resources and rapid car ownership makes the traffic signal timing become a pivotal challenge. Emerging studies have been carried out on adaptive signal timing, but most of them still focus on the throughput of intersections, leaving safety and travel experience unconsidered. This paper proposes a time difference penalized traffic signal timing method by reinforcement learning technique to balance safety and throughput capacity in traffic control system. Firstly, a microcosmic state representation is proposed to integrate the dynamics of both traffic lights and road vehicles, including driver behaviors of lane changing, car-following, previous phase of traffic light and its duration. Secondly, an action space, including 8 signal phases, and a behavior-aware reward function are designed to resist the red-light overflow. Finally, a partial long short term memory (LSTM) network is trained to balance traffic efficiency and traveling experience. In the network training, a parallel sampling method is adopted to obtain experience from multiple environments to accelerate the training convergence in practical application. Experimental results show that the proposed method improves the intersection efficiency up to 14.28% compared to the fixed signal timing and 5.26% compared to DQN while getting rid of red-light overflow time.

show abstract

Section: System Modeling For Rep-drqn 1) State Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

Liao

Liu

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…( + 1) = e ( ) + e ( ) + e (11) where ( ) denotes the control variable of acceleration at prediction time and e = … + y 0 −1 0 0 0…”

Section: Longitudinal Controlmentioning

confidence: 99%

“…The state dynamics for lateral control is similar to (11). But unlike the definition in longitudinal control which uses the current state of the front vehicle as a reference, the history path of the leading vehicle is used.…”

Section: Lateral Controlmentioning

confidence: 99%

See 1 more Smart Citation

Development and verification of cooperative adaptive cruise control via LTE‐V

Wang

Chen

Wang

et al. 2019

IET Intelligent Transport Systems

View full text Add to dashboard Cite

In this paper, we present a testbed platform for realizing cooperative adaptive cruise control (CACC) enabled by LTE-V (LTE-vehicle). The platform is developed on a platoon of vehicles, each of which is equipped with a suite of on-board sensing and computing devices for environment perception and automated vehicle control, as well as an LTE-V transceiver for high-performance vehicle-to-vehicle (V2V) communication. The hardware architecture and software architecture, especially the perception and control methods, of the platform are described. Field experiments in different road conditions are conducted to verify the feasibility of our platform. The results also show the potential of V2V communications via LTE-V in terms of improving the sensing capability of individual vehicle's on-board sensors.2 information (e.g., speed, wheel angle, heading and location, throttle and brake actions, etc.) to achieve better environment perception, has attracted wide research attentions. The dedicated short-range communications (DSRC) technology has been considered as the solution to realizing V2X communication for years. The US Federal Communication Commission (FCC) has allocated a 75 MHz spectrum in the 5.9 GHz band for DSRC [25]. The information exchanged among vehicles can be encoded into Basic Safety Message (BSM) and periodically broadcasted by vehicles according to the Society of Automotive Engineering (SAE) standard [26]. However, due to the ad hoc working nature, DSRC may not be able to fully guarantee highly-reliable and low-delay communication in all traffic conditions.In the past few years, the cellular-based LTE-V (LTE vehicle) technology [27] has started to be investigated in many countries and research communities. For example, a data exchange standard [28] is currently being developed by the Society of Automotive Engineers of China (SAE-China). Cooperative automated driving is seen as one of the most important use cases supported by LTE-V and LTE-eV2X (the next generation of LTE-V) according to 3GPP TR 22.886 [29]. LTE-V supports both V2V (vehicle-to-vehicle) communication over the peer-to-peer PC5 interface, and V2N (vehicle-to-network) communication over the LTE-Uu interface. Therefore, the interference management and scheduling of V2V communication traffic can be assisted by base stations to obtain a more reliable and efficient message dissemination compared with DSRC, especially in the highly mobile environment. The transmission delay of LTE-V is expected to be about 25ms through V2V direct communication, which meets the latency requirements of platooning defined in 3GPP TR 22.886. A comparative analysis between LTE-V and DSRC [30] is shown in Table 1, which clearly demonstrates the advantages of the former technology.

show abstract

Integrated energy-oriented cruising control of electric vehicle on highway with varying slopes considering battery aging

Zhuang

et al. 2019

Sci. China Technol. Sci.

View full text Add to dashboard Cite

Minimize the Fuel Consumption of Connected Vehicles Between Two Red-Signalized Intersections in Urban Traffic

Cited by 59 publications

References 24 publications

Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

Development and verification of cooperative adaptive cruise control via LTE‐V

Integrated energy-oriented cruising control of electric vehicle on highway with varying slopes considering battery aging

Contact Info

Product

Resources

About