scholarly journals Driver Characteristics Oriented Autonomous Longitudinal Driving System in Car-Following Situation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6376
Author(s):  
Haksu Kim ◽  
Kyunghan Min ◽  
Myoungho Sunwoo
Keyword(s):  
Driving Behavior ◽  
Cruise Control ◽  
Driver Assistance ◽  
Collision Warning ◽  
Driving System ◽  
Traffic Jam ◽  
Car Following ◽  
Preceding Vehicle ◽  
Planning Algorithm ◽  
Control Traffic

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.

Findings on the Approach Process Between Vehicles: Implications for Collision Warning

2000 ◽  
Vol 1724 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Mark A. Brackstone ◽  
Beshr Sultan ◽  
Michael McDonald
Keyword(s):  
Cruise Control ◽  
Collision Warning ◽  
Time To Collision ◽  
Car Following ◽  
Brief Assessment ◽  
Road Design ◽  
Equal Importance ◽  
Instrumented Vehicle ◽  
Transport Telematics ◽  
Few Data

Over the past 10 years there has been a growing body of research into modeling and describing driving behavior, particularly for situations that occur on motorways. This interest has arisen from the need to assess safety and capacity benefits that could be produced by changes to road design, operation, signage, and in-vehicle advanced transport telematics, such as collision warning (CW) or autonomous cruise control. For the most part these investigations have focused on “close” or “car” following, which describes the maintenance of a time- or distance-based following headway. However, often overlooked, and of equal importance, is the “approach” process, describing how a driver decelerates when approaching a slower vehicle. There are several competing theories of the behavioral basis underlying this process, including, for example, those based on time-to-collision or optic flow. There are, however, very few data against which such models can be assessed and systems designed. Presented are the results from an exploratory, instrumented vehicle study designed to assess approach mechanisms. The two key features of the process are explored: the circumstances under which driver deceleration is instigated, and the process governing the control of the deceleration itself. Finally, there is a brief assessment of the implications of these findings for the design of CW systems, in which realistic warnings may prove vital to their acceptance by the driving public.

scholarly journals Adaptive cruise control system with traffic jam tracking function based on multi-sensors and the driving behavior of skilled drivers

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879580
Author(s):  
Guoxin Zhang ◽  
Zengcai Wang ◽  
Baiwang Fan ◽  
Lei Zhao ◽  
Yazhou Qi
Keyword(s):  
Control System ◽  
Adaptive Cruise Control ◽  
Driving Behavior ◽  
Cruise Control ◽  
Adaptive Cruise Control System ◽  
Traffic Jam ◽  
Laser Sensors ◽  
Cruise Control System ◽  
Stationary Objects ◽  
Scope Of Application

The traditional adaptive cruise control system generally requires 25–40 km/h velocity to function. Moreover, the adaptive cruise control system cannot decelerate to the stop state, cannot adjust for stationary objects, and has limited scope of application. This study achieved the traffic jam tracking function of vehicles through the simultaneous use of millimeter wave and laser sensors and the analysis of the driving behavior of skilled drivers. The spacing and acceleration control of a vehicle is optimized based on the premise of ensuring safety and comfort by providing smooth, comfortable, safe, and radical control modes for driver selection, thereby increasing the probability that adaptive cruise control adopted by drivers. In addition, the collision avoidance function is added for safety reasons. Finally, actual vehicle experiments show that the distance and acceleration errors are in the expected range of errors of drivers. Moreover, the validity and practicability of the proposed adaptive cruise control algorithm are verified.

scholarly journals Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening

2020 ◽  
Vol 10 (5) ◽  
pp. 1635
Author(s):  
Lie Guo ◽  
Pingshu Ge ◽  
Dachuan Sun ◽  
Yanfu Qiao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Car Following ◽  
Control Precision ◽  
Performance Indexes ◽  
Preceding Vehicle ◽  
And Control

In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.

scholarly journals Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4020 ◽  
Author(s):  
Gyubin Sim ◽  
Kyunghan Min ◽  
Seongju Ahn ◽  
Myoungho Sunwoo ◽  
Kichun Jo
Keyword(s):  
Electric Vehicles ◽  
Driving Behavior ◽  
Regenerative Braking ◽  
Multi Layer Perceptron ◽  
Time To Collision ◽  
Car Following ◽  
Braking System ◽  
Planning Algorithm ◽  
Autonomous Version ◽  
The Individual

The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance.

scholarly journals Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Mudasser Seraj ◽  
Jiangchen Li ◽  
Zhijun Qiu
Keyword(s):  
Mixed Traffic ◽  
Cruise Control ◽  
Automated Driving ◽  
Driving System ◽  
Car Following ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control ◽  
Traffic Stream ◽  
Car Following Model ◽  
The Cost

Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations.

Platoon of SAE Level-2 Automated Vehicles on Public Roads: Setup, Traffic Interactions, and Stability

2019 ◽  
Vol 2673 (9) ◽  
pp. 311-322 ◽  
Author(s):  
Victor L. Knoop ◽  
Meng Wang ◽  
Isabel Wilmink ◽  
D. Marika Hoedemaeker ◽  
Mark Maaskant ◽  
...  
Keyword(s):  
Field Tests ◽  
Market Penetration ◽  
Cruise Control ◽  
Driver Assistance ◽  
Automated Vehicles ◽  
Driver Assistance Systems ◽  
Car Following ◽  
Traffic Conditions ◽  
Assistance Systems ◽  
Level 2

An increasing amount of vehicles are equipped with driver assistance systems; many of the vehicles currently on the market can be optionally equipped with adaptive cruise control and lane centering systems. Using both systems at the same time brings the vehicle to SAE level-2 automation . This means a driver does not need to perform longitudinal and lateral operational driving, although the driver should be ready to intervene at any time. While this can provide comfort, the interaction between vehicles operated by these systems might cause some undesired effects. This becomes particularly relevant with increasing market penetration rates. This paper describes an experiment with seven SAE level-2 vehicles driven as a platoon on public roads for a trip of almost 500 km. The paper discusses how the experiment was organized and the equipment of the vehicles. It also discusses the interaction of the platoon in traffic, as well as, in basic terms, the interaction between the automated vehicles. The experiences can be useful for other studies setting up field tests. The conclusion from this platoon test is: intentionally creating platoons on public roads is difficult in busy traffic conditions. Moreover, interactions between the vehicles in the platoon show that the current SAE level-2 systems are not suitable for driving as platoons of more than typically three to four vehicles, because of instabilities in the car-following behavior.

scholarly journals Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm

2020 ◽  
Vol 10 (15) ◽  
pp. 5271
Author(s):  
Zifei Nie ◽  
Hooman Farzaneh
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Car Following ◽  
Preceding Vehicle ◽  
Variation Rate ◽  
Model Predictive Control Algorithm

An adaptive cruise control (ACC) system is developed based on eco-driving for two typical car-following traffic scenes. The ACC system is designed using the model predictive control (MPC) algorithm, to obtain objectives of eco-driving, driving safety, comfortability, and tracking capability. The optimization of driving comfortability and the minimization of fuel consumption are realized in the manner of constraining the acceleration value and its variation rate, so-called the jerk, of the host vehicle. The driving safety is guaranteed by restricting the vehicle spacing always larger than minimum safe spacing from the host vehicle to the preceding vehicle. The performances of the proposed MPC-based ACC system are evaluated and compared with the conventional proportional-integral-derivative (PID) controller-based ACC system in two representative driving scenarios, through a simulation bench and an instantaneous emissions and fuel consumption model. In addition to meeting the other driving objectives mentioned above, the simulation results indicate an improvement of 13% (at the maximum) for fuel economy, which directly shows the effectiveness of the presented MPC-based ACC system.

scholarly journals A Comparison Analysis of Surrogate Safety Measures with Car-Following Perspectives for Advanced Driver Assistance System

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Sehyun Tak ◽  
Sunghoon Kim ◽  
Donghoun Lee ◽  
Hwasoo Yeo
Keyword(s):  
Warning System ◽  
Safety Measure ◽  
Driver Assistance ◽  
Collision Risk ◽  
Driver Assistance Systems ◽  
Car Following ◽  
Emergency Braking ◽  
Driving Behaviour ◽  
Preceding Vehicle ◽  
Forward Collision Warning

Surrogate Safety Measure (SSM) is one of the most widely used methods for identifying future threats, such as rear-end collision. Various SSMs have been proposed for the application of Advanced Driver Assistance Systems (ADAS), including Forward Collision Warning System (FCWS) and Emergency Braking System (EBS). The existing SSMs have been mainly used for assessing criticality of a certain traffic situation or detecting critical actions, such as severe braking maneuvers and jerking before an accident. The ADAS shows different warning signals or movements from drivers’ driving behaviours depending on the SSM employed in the system, which may lead to low reliability and low satisfaction. In order to explore the characteristics of existing SSMs in terms of human driving behaviours, this study analyzes collision risks estimated by three different SSMs, including Time-To-Collision (TTC), Stopping Headway Distance (SHD), and Deceleration-based Surrogate Safety Measure (DSSM), based on two different car-following theories, such as action point model and asymmetric driving behaviour model. The results show that the estimated collision risks of the TTC and SHD only partially match the pattern of human driving behaviour. Furthermore, the TTC and SHD overestimate the collision risk in deceleration process, particularly when the subject vehicle is faster than its preceding vehicle. On the other hand, the DSSM shows well-matched results to the pattern of the human driving behaviour. It well represents the collision risk even when the preceding vehicle moves faster than the follower one. Moreover, unlike other SSMs, the DSSM shows a balanced performance to estimate the collision risk in both deceleration and acceleration phase. These research findings suggest that the DSSM has a great potential to enhance the driver’s compliance to the ADAS, since it can reflect how the driver perceives the collision risks according to the driving behaviours in the car-following situation.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

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