Ecological Adaptive Cruise Control for Urban Environments using SPaT Information

Chada, Sai Krishna; Purbai, Ankith; Görges, Daniel; Ebert, Achim; Teutsch, Roman

doi:10.1109/vppc49601.2020.9330903

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…The research on vehicle speed planning directly affects issues such as energy consumption, traffic efficiency, and ride smoothness during vehicle operation. Currently, there is extensive research on speed planning for Adaptive Cruise Control (ACC), aimed at reducing energy consumption during vehicle operation [5][6]. Additionally, some studies have established a connection between vehicles and traffic signals based on V2I communication, to enable passing through signal intersections without stopping, thus achieving short travel times and low energy consumption [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

Ning,

Tian,

Lin

et al. 2024

IEEE Access

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This research delves deeply into the traffic light intersection control issue for connected autonomous vehicles (CAVs). Firstly, we employed the vehicle dynamics simulation software, CarSim, to model the vehicle, and utilized the intelligent driving simulation software, PreScan, to establish a road environment model. Subsequently, we designed a rule-based traffic light crossing controller (RB-TLCC) for CAVs in Matlab/Simulink and implemented a co-simulation with CarSim and PreScan. Furthermore, we conducted driver-in-the-loop studies using the Logitech G27 driving simulator kit and compared the experimental results with those from RB-TLCC. Our findings indicate that the RB-TLCC enhances regenerative braking energy by 23.5%, improves traffic efficiency by 17.9%, and increases driving smoothness by 50.7%. Additionally, based on Markov Chain theory, we proposed a multi-objective optimization model (MO-OM) for CAVs traffic light intersection crossing using the Proximal Policy Optimization algorithm (PPO). A comparison was conducted under two different operating conditions between RB-TLCC and dynamic programming algorithms (DP). The results indicate that the MO-OM proposed in this paper exhibits the best comprehensive control performance among the three control methods. While adhering to traffic regulations, it enhances the recuperation of braking energy, the efficiency of vehicle passage, and the smoothness of travel at traffic light intersections. This study offers effective methodologies for enhancing the performance of CAVs in traffic light intersection control and holds significant reference value for the advancement of future intelligent transportation systems (ITS).INDEX TERMS Connected autonomous vehicles, intelligent traffic system, multi-objective optimization, optimal speed planning, PPO algorithm, traffic light intersection control.

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Section: Introductionmentioning

confidence: 99%

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

Ning,

Tian,

Lin

et al. 2024

IEEE Access

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“…The influence due to other important factors in the urban environments such as traffic light (TL) signals, bus stops and stop signs have often been neglected in the literature. An effort was made in our previous work [10] to propose a novel EACC concept to minimize the energy consumption in an electric vehicle (EV) by taking the advantage of signal phase and timing (SPaT) information from traffic signals. Recent advancements in the intelligent transportation systems (ITS) and vehicleto-infrastructure (V2I) technology have made it possible to communicate between urban vehicles, signalized intersections and road infrastructure in real-time.…”

Section: Introductionmentioning

confidence: 99%

Ecological Adaptive Cruise Control for City Buses based on Hybrid Model Predictive Control using PnG and Traffic Light Information

Chada,

Thomas,

Görges

et al. 2021

Preprint

Self Cite

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This paper proposes an ecological adaptive cruise control (EACC) concept with the primary goal to minimize the fuel consumption in a city bus with an internal combustion engine (ICE). A hybrid model predictive control (HMPC) is implemented in this work to control both continuous and discretetime variables. Moreover, a multi-objective optimization problem for EACC is formulated in time-domain as a mixed-integer quadratically constrained quadratic programming (MIQCQP) problem. The proposed HMPC-EACC performs robust vehiclefollowing while tracking a leading vehicle and plans fuel-efficient acceleration and deceleration maneuvers for the host vehicle. Additionally, it uses the signal phase and timing (SPaT) information to compute a green wave reference speed for the host vehicle to cross the signalized intersections at a green phase. Moreover, the proposed controller performs pulse and glide (PnG) to optimally control the engine ON and OFF states and save additional fuel. Furthermore, the performance of the proposed strategy is evaluated on a real-world driving profile and compared against a baseline controller from the literature. Finally, the influence of different prediction horizons on the fuel savings and computation times are studied. The results reveal significant reduction in fuel consumption with HMPC-EACC and demonstrate that the proposed controller is real-time capable.

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“…Due to the dependence on predicted future route information, offline planning strategies have shown poor results against unexpected disturbances. To enhance the robustness, real-time eco-driving optimisation strategies have been developed to minimise the energy consumption of CAVs considering uncertainties from other traffic participants [15], [16]. This enables the CAV to achieve higher energy efficiency whilst maintaining safe inter-vehicular distances.…”

mentioning

confidence: 99%

Eco-Driving of General Mixed Platoons With CAVs and HDVs

Yang

Zhao

Lan

et al. 2023

IEEE Trans. Intell. Veh.

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Eco-driving has been widely investigated over the last decade, but most studies focused on an individual vehicle or a vehicle platoon consisting of pure connected and automated vehicles (CAVs). Recently, mixed vehicle platoons consisting of both CAVs and human-driven vehicles (HDVs) have attracted much interest, considering the fact that HDVs will mix with CAVs in the traffic system for a long period. This paper proposes an eco-driving strategy for mixed platoons, composing of both offline planning and online tracking. In offline planning, an energy-efficient speed reference and a gearshift reference are determined by using the characteristics of each vehicle and future traffic information through dynamic programming. Offline planning optimised the vehicle speed and gearshift to allow the vehicle powertrain working at a high efficiency region. In online tracking, two different types of model predictive controls (MPCs) are proposed to control the CAVs in real-time. The MPCs are designed to achieve precise speed reference tracking performance and guarantee platoon string stability, respectively. Meanwhile, HDVs within the mixed platoon can be located anywhere in the platoon except working as the first vehicle to improve flexibility. Therefore, the proposed eco-driving strategy is applicable to mixed platoons with more general structures in ordering. The key contribution of this study is that the proposed eco-driving strategy can optimise the total fuel consumption for general mixed platoons. Simulation results show that the proposed ecodriving strategy improves the fuel economy of a mixed platoon by up to 6.39% compared to the benchmark conventional-adaptive cruise control strategies.

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Ecological Adaptive Cruise Control for Urban Environments using SPaT Information

Cited by 10 publications

References 12 publications

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

Ecological Adaptive Cruise Control for City Buses based on Hybrid Model Predictive Control using PnG and Traffic Light Information

Eco-Driving of General Mixed Platoons With CAVs and HDVs

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