On-ramp merging is a critical bottleneck in freeway traffic flow, contributing to congestion, accidents, and excessive fuel consumption. Although traditional ramp metering provides macroscopic control, it lacks the granularity for optimizing an individual vehicle’s trajectory. Cooperative merging, enabled by connected and automated vehicles, can potentially enhance traffic efficiency, safety, and fuel economy. However, existing research often neglects the influence of heterogeneous vehicle dynamics, unreliable vehicle-to-vehicle (V2V) communication, and real-time implementation challenges. This paper introduces novel model-free online speed planners for cooperative on-ramp merging. The planners address these limitations by being agnostic to vehicle dynamics, effectively compensating for V2V communication packet drops and incurring only a light computational burden. Comprehensive evaluation, conducted on a real-time traffic-vehicle-communication co-simulation platform integrating high-fidelity vehicle dynamics, a traffic simulator, and recorded V2V communication footprints, demonstrates the effectiveness of the proposed speed planners. Simulation results reveal that the proposed method yields accurate tracking of desired speed and inter-vehicle distance, maintaining low fuel consumption even under high packet drop ratios, and demonstrating real-time implementation efficiency.

Fatigue driving has been regarded as one of the most important factors that cause traffic accidents. This paper proposes a robust human-machine shared control strategy to improve the vehicle performance for different driver fatigue states. Firstly, the time-varying driver steering model is proposed to address the model mismatch caused by fatigue driving. And the driver fatigue evaluation system is established based on facial features to quantify driver fatigue levels. Based on the quantified fatigue levels, a novel strategy for allocating authorities of the driver and controller is developed for building the driver-vehicle interaction system. Then, to weaken the influence of parameter perturbations caused by the time-varying driver states, we design a fatigue-based shared controller through state feedback. The actuator saturation and system constraints are considered in the controller design through the robust set-invariance property to improve vehicle safety and driving comfort. The driver-in-the-loop platform is conducted to validate the effectiveness of the proposed shared steering controller. The experimental results show that the proposed strategy can adaptively optimize the human-machine authorities according to fatigue states and comprehensively improve vehicle performance.

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Various cooperative merging control strategies at on-ramp have been proposed in the last decade. Approximated vehicle longitudinal motion models, e.g., kinematics model, have been broadly adopted for controller synthesis because of their simplicity. However, what appears problematic is that the models used for controller validation remain, in many cases, the same as the ones used for controller design. Indeed, actual vehicle dynamics contain rich behaviors that the simplified models cannot fully cover. In this paper, we first demonstrate that the actual vehicle speed can be dissimilar to the reference from a speed planner once vehicle dynamics is considered. Then, we propose two data-driven speed generators agnostic to vehicle dynamics. SUMO/Simulink joint simulations demonstrate that the proposed reference speed planners can successfully merge vehicles with distinct dynamics characteristics by following the desired sequence, speed, and intervehicle distance at the merging point while avoiding collisions.

Various collision-avoidance assistance (CAA) systems, such as automatic emergency braking (AEB) and lane-keeping assistance (LKA), have been developed in the last decades to enhance the active safety of ground vehicles. Meanwhile, more electronic computing units (ECUs) have been embedded inside a vehicle to support the diversified CAA systems, which complicate the automotive electrical/electronic architecture and increase the cost. Instead of addingextraECUs, we propose to allocate theexistingimplementation resources, i.e., the available processor time and memory space to the CAA systems, per individual driver’s maneuver capabilities. As an illustrative example, we first show that two drivers can exhibit distinct maneuvers in a pre-crash situation on highway, according to which they can be classified as either steering-oriented or braking-oriented. Then, we design two CAA systems: an AEB and an LKA, based on the ultra-local model predictive control method. Furthermore, we show that by adjusting the prediction horizons of the two controllers, the implementation resources can be allocated to the two CAA systems in different fashions, which yields three control modes: standard mode, steering-enhanced mode, and braking-enhanced mode. Finally, by comparing the control performance of each driver-type/control-mode pair through both CarSim-Simulink joint simulations and driver-in-the-loop simulator experiments, we demonstrate that by allocating more resources to compensate for the weakness of a driver’s maneuver, the CAA systems can provide enhanced driving safety by consuming the same overall amount of the implementation resources.

Car-following models describe how a driver follows the leading vehicle in the same lane. They serve as the cornerstone of microscopic traffic-flow simulations and play an essential role in analyzing human factors in traffic casualty, congestion, efficiency, and emissions. An extensive and continuously growing number of car-following models in the literature raises the requirement to evaluate and compare different models objectively. Generally, a car-following model is evaluated after model parameter calibration: the optimal residual between the calibrated model output and the measured counterpart is used as a metric to assess a car-following model’s performance. However, model parameter calibration, usually formed as a numerical optimization problem, suffers from several issues, such as local optimality and heavy computational burden. More importantly, different formulations of the cost function can lead to distinct calibration outcomes and contradictory conclusions of the model evaluation results. This paper proposes instead a purely algebraic framework for evaluating a class of car-following models whose parameters can be linearly identified. Car-following models with nonlinear relationships among parameters, e.g., the behavioral car-following models, are out of the scope of analysis in this paper. Algebraic manipulations performed on a model finally produce a system error index, which is a uniform metric for evaluating and comparing different car-following models. During the whole process, no cost function needs to be designed a priori, and no computationally expensive numerical optimization is involved. Three car-following models are evaluated and compared under the proposed algebraic framework.

Due to the simultaneous existence of model uncertainties and external disturbances, designing automated ground vehicle path-following controllers is recognized as a challenging task. The$H_{\infty }$robust control methodology, as one of the accomplished strategies for controller robustification, has been commonly adopted by researchers to address the vehicle path-tracking problems. Nevertheless, despite its advantages, the$H_{\infty }$controller is only capable of limiting the total “energy” of the tracking errors. On the other hand, from a safety standpoint, constraining the “peak” of the tracking errors may carry an equal or more importance. To establish a guaranteed upper bound on the path-tracking errors, this paper proposes a novel methodology to synthesis the ground vehicle path-following controller in light of the energy-to-peak robust control theory. Additionally, to address the time-varying uncertainties presented in the tire dynamics, robust stabilization constraints based upon the small-gain theorem are also formulated into the overall controller design problem. Comparative study regarding the disturbance rejection performance between the proposed controller and the conventional$H_{\infty }$approach is conducted via CarSim-Simulink joint simulations. Furthermore, the robustness and disturbance attenuation ability of the energy-to-peak path-tracking controller is experimentally verified on a scaled car.

The ability to detect lane markings under varying lighting conditions is essential for autonomous mobile robots and automatic vehicle driving assistance systems. Because the object color information is subject to illumination variation, this article presents a novel and computationally efficient algorithm based on the extremum-seeking method to achieve illumination-resilient lane detection and path-following tasks for autonomous driving. Lane detection is performed in the hue-saturation-value color space by distinguishing the colored lane marks from the background. The system’s inputs are the upper and lower thresholds in each of the hue, saturation, and value channels. We define a cost function as the combination of detection accuracy and lane coverage to evaluate the algorithm performance. Two extremum-seeking schemes, one with fixed dither amplitudes and another with adaptive dither amplitudes, are designed to adjust the system inputs to minimize the cost function. The two proposed methods are validated and compared through video-based simulation studies and scaled-car field experimental tests.

Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced$\mathcal {L}^{\mathbf {1+\alpha }}$tracking performance. Second, a$\mathcal{C}^{ \boldsymbol {\infty }}$-differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.

Trajectory following of autonomous vehicle is a challenging task because of the multiple constraints imposed on the plant. Therefore, Model Predictive Control (MPC) is becoming prevail in vehicle motion control as it can explicitly handle system constraints. However, MPC, grounded in real-time iterative optimization, entails a considerable computational burden for current electronic control units. To mitigate the MPC execution load, a popular strategy is to linearize the original (nonlinear) system around the current working point and then design a Linear Time-Varying MPC (LTVMPC). Nevertheless, the successive linearization introduces extra modeling errors, which may impair the control performance. Indeed, if the plant model satisfies the `differential flatness' condition, it can be exactly linearized to the Brunovsky's canonical form. In contrast to the LTV model, this newly appeared linear form reserves all the nonlinear features of the native plant model. Based on this equivalent linear system, a Flatness Model Predictive Controller (FMPC) can be formulated. FMPC on the one hand, improves the control performance over an LTVMPC because it avoids extra modeling errors from the local linearization. On the other hand, it entails a much lighter computational load versus a nonlinear MPC thanks to its linear nature. Real-time simulations conducted on a hardware-in-the-loop system indicate the advantages of the proposed FMPC in autonomous vehicle trajectory following.

The rapid growth of autonomous driving in recent years has posed some new research challenges to the traditional vehicle control system. For example, in order to flexibly change the yawing rate and moving speed of a vehicle based on the detected road conditions, autonomous driving control often needs to dynamically tune its control parameters for better trajectory tracking and vehicle stability. Consequently, the execution time of driving control can increase significantly, resulting in missing the end-to-end (E2E) deadline from detection to computation and actuation, and thus possible accidents.In this paper, we propose AutoE2E, a two-tier real-time middleware system that helps the automotive OS meet the E2E deadlines of all the tasks despite execution time variations, while achieving the maximum possible computation precision (and thus minimum tracking errors) for driving control. The inner loop of AutoE2E dynamically controls the CPU utilizations of all the on-board processors to stay below their respective schedulable utilization bounds, by adjusting the invocation rates of the vehicle tasks running on those processors. The outer loop is designed to adapt the computation time and precision of driving control, when the inner loop loses its control capability due to rate saturation caused by vehicle speed changes. Our evaluations, both on a hardware testbed with scaled cars and in larger-scale simulation, show that AutoE2E can effectively reduce the deadline miss ratio by 35.4% on average, compared to well-designed baselines, while having smaller precision loss and tracking errors.

Personalized driving assistance system for vehicle collision avoidance has recently received a considerable amount of attention. Consensus has been reached that both the overall driver-vehicle control performance and the driver acceptance can be increased by embedding individual driver preferences and characteristics into the assistance system design. However, the majority of the existing personalized controllers has not yet taken the available computational resource into account. Indeed, as stricter requirements on emissions, safety, and vehicle connectivity drastically complicate the automotive electronics, it becomes common to aggregate several functions inside one single computing unit. Function consolidation simplifies electronic architecture and saves costs. However, it aggravates the competition for computational resources among different applications. Therefore, this paper proposes a novel perspective for personalized driving assistance system design through computational resource re-allocation. For a driver inherently adept at longitudinal (or lateral) control and less capable of lateral (or longitudinal) control, a stronger support from the collision avoidance system and the underlying computational resource can be allocated towards steering (or braking) assistance by this design. Carsim-Simulink conjoint simulations demonstrate that the overall driver-vehicle control performance can be substantially improved with the same computational resource consumption.

In a Vehicular Cyber Physical System (VCPS), ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, then safety messages can have a much better chance to meet their real-time deadlines. In this article, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. During normal driving, MC-Safe monitors periodic beacons sent by other vehicles and estimates the communication delay on all the channels. Upon the detection of a potential accident, MC-Safe leverages a novel channel negotiation scheme that allows all the involved vehicles to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. MC-safe also features a novel coordinator selection algorithm that minimizes the delay of channel negotiation. Once a channel is selected, all the involved vehicles switch to the same selected channel for real-time communication with the least amount of interference. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 23.4% lower packet delay on average compared with other well-designed channel selection baselines.

Autonomous vehicles often need human driver to take over in some complicated conditions. Such a sudden takeover could jeopardize the vehicle's safety and stability if not han-dled properly. Hence, if the driver's takeover intention can be recognized as early as possible, the vehicle can have sufficient time to make important takeover preparation. The existing in-car monitoring systems are mostly based on camera, which have several key limitations, such as brightness condition and motion obscurity. On the other hand, WiFi-based wireless sensing has recently shown a great promise in human activity recognition, but mainly for large-scale movements performed in the room environment. In this paper, we propose WiDrive, a real-time in-car driver activity recognition system based on Channel State Information (CSI) changes of WiFi signals. WiDrive consists of three major components: A novel algorithm to extract small-scale in-car human activity features, a real-time recognition system based on Hidden Markov Model (HMM), and an online adaptation algo-rithm to adapt for different drivers and vehicles. We implement WiDrive with commercial WiFi devices and evaluate it in real cars. Our results show that WiDrive has an average recognition accuracy of 91.3% and improves the takeover safety.

Ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, safety messages can have a much better chance to meet their real-time deadlines. In this paper, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. MC-Safe features a novel channel negotiation scheme that allows all the vehicles involved in a potential accident to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 12.31% lower deadline miss ratio and an 8.21% higher packet delivery ratio on average.