This paper demonstrates that the acceleration/deceleration limits in ACC systems can make a string stable ACC amplify the speed perturbation in natural driving. It is shown that the constrained acceleration/deceleration of the following ACCs are likely to cause speed overshoot to compensate for an extra large/small spacing. Additionally, we find that the constrained deceleration limits can also jeopardize safety, as the limited braking power produces extra small spacing or even crashes. The findings are validated through experiments on real cars. The paper suggests that the ACC parameter space should be extended to include the acceleration/deceleration limits considering their significant role exposed here. Through numerical simulations of ACC platoons, we show i) a marginal string stable ACC is preferable due to the smaller total queue length and the shorter duration in congestion; ii) congestion waves in a mixed ACC platoon largely depend on the sequence of vehicles provided different acceleration/deceleration limits, and iii) the safety hazard caused by the constrained deceleration limits is more significant in mixed ACC platoons when string unstable ACCs exist.
This paper presents an adaptive leading cruise control strategy for the connected and automated vehicle (CAV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where CAVs and HDVs share the road, the longitudinal speed control of CAVs can substantially reduce the energy consumption of the following H…
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 discrete-time 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 vehicle-following while tracking a leading vehicle and plans fuel-efficient acce…
This paper studies the value of communicated motion predictions in the longitudinal control of connected automated vehicles (CAVs). We focus on a safe cooperative adaptive cruise control (CACC) design and analyze the value of vehicle-to-vehicle (V2V) communication in the presence of uncertain front vehicle acceleration. The interest in CACC is motivated by the potential improvement in energy consumption and road throughput. In order to quantify this potential, we characterize experimentally the relationship between inter-vehicular gap, vehicle speed, and (reduction of) energy consumption for a…
Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration, with each other. Using sensors such as cameras, radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected, as well as the relative speed. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, 3 car following…
This study compares Deep Reinforcement Learning (DRL) and Model Predictive Control (MPC) for Adaptive Cruise Control (ACC) design in car-following scenarios. A first-order system is used as the Control-Oriented Model (COM) to approximate the acceleration command dynamics of a vehicle. Based on the equations of the control system and the multi-objective cost function, we train a DRL policy using Deep Deterministic Policy Gradient (DDPG) and solve the MPC problem via Interior-Point Optimization (IPO). Simulation results for the episode costs show that, when there are no modeling errors and the t…
In this paper, we design decentralized control strategies for the two-dimensional movement of autonomous vehicles on lane-free roads. The bicycle kinematic model is used to model the dynamics of the vehicles, and each vehicle determines its control input based only on its own speed and on the distance from other (adjacent) vehicles and the boundary of the road. Potential functions and Barbalat's lemma are employed to prove the following properties, which are ensured by the proposed controller: (i) the vehicles do not collide with each other or with the boundary of the road; (ii) the speeds of …
Cooperative Adaptive Cruise Control with Constant Distance Gaps for Retrofitted Legacy Vehicles
Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa（比萨大学能源、系统、领土与建筑工程系）；Fraunhofer Institute FOKUS, Berlin（柏林弗劳恩霍夫FOKUS研究所）；Dyson School of Design Engineering, Imperial College London（帝国理工学院戴森设计工程学院）
A phase-aware AI car-following model for electric vehicles with adaptive cruise control: Development and validation using real-world data
Department of Civil Engineering（土木工程系）；Saint Louis University（圣路易斯大学）；Department of Civil, Environmental, and Architectural Engineering（土木、环境与建筑工程系）；University of Kansas（堪萨斯大学）；Department of Computer Science（计算机科学系）
Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and…
In a typical car-following scenario, target vehicle speed fluctuations act as an external disturbance to the host vehicle and in turn affect its energy consumption. To control a host vehicle in an energy-efficient manner using model predictive control (MPC), and moreover, enhance the performance of an ecological adaptive cruise control (EACC) strategy, forecasting the future velocities of a target vehicle is essential. For this purpose, a deep recurrent neural network-based vehicle speed prediction using long-short term memory (LSTM) and gated recurrent units (GRU) is studied in this work. Bes…
