scholarly journals Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2259 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang
Keyword(s):  
Risk Perception ◽  
Autonomous Vehicles ◽  
Decision Model ◽  
Signal Detection Theory ◽  
Critical Issue ◽  
Lane Change ◽  
Mixed Traffic ◽  
Driving System ◽  
Lane Changes ◽  
The Subject

Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver’s perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle’s Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s2 and 1.76 m/s2, respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle’s driver’s safety.

Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS

2019 ◽  
Vol 2673 (8) ◽  
pp. 298-309 ◽  
Author(s):  
Ishtiak Ahmed ◽  
Alan Karr ◽  
Nagui M. Rouphail ◽  
Gyounghoon Chun ◽  
Shams Tanvir
Keyword(s):  
Autonomous Vehicles ◽  
Lateral Displacement ◽  
Low Cost ◽  
Detection Algorithm ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Lane Changes ◽  
Detection Systems ◽  
Level Information

With the expected increase in the availability of trajectory-level information from connected and autonomous vehicles, issues of lane changing behavior that were difficult to assess with traditional freeway detection systems can now begin to be addressed. This study presents the development and application of a lane change detection algorithm that uses trajectory data from a low-cost GPS-equipped fleet, supplemented with digitized lane markings. The proposed algorithm minimizes the effect of GPS errors by constraining the temporal duration and lateral displacement of a lane change detected using preliminary lane positioning. The algorithm was applied to 637 naturalistic trajectories traversing a long weaving segment and validated through a series of controlled lane change experiments. Analysis of naturalistic trajectory data revealed that ramp-to-freeway trips had the highest number of discretionary lane changes in excess of 1 lane change/vehicle. Overall, excessive lane change rates were highest between the two middle freeway lanes at 0.86 lane changes/vehicle. These results indicate that extreme lane changing behavior may significantly contribute to the peak-hour congestion at the site. The average lateral speed during lane change was 2.7 fps, consistent with the literature, with several freeway–freeway and ramp–ramp trajectories showing speeds up to 7.7 fps. All ramp-to-freeway vehicles executed their first mandatory lane change within 62.5% of the total weaving length, although other weaving lane changes were spread over the entire segment. These findings can be useful for implementing strategies to lessen abrupt and excessive lane changes through better lane pre-positioning.

An optimal mandatory lane change decision model for autonomous vehicles in urban arterials

2017 ◽  
Vol 21 (4) ◽  
pp. 271-284 ◽  
Author(s):  
Peng Cao ◽  
Yubai Hu ◽  
Tomio Miwa ◽  
Yukiko Wakita ◽  
Takayuki Morikawa ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Decision Model ◽  
Lane Change ◽  
Urban Arterials

Driver Behavior at a Freeway Merge to Mixed Traffic of Conventional and Connected Autonomous Vehicles

2020 ◽  
Vol 2674 (11) ◽  
pp. 867-874
Author(s):  
Sneha Chityala ◽  
John O. Sobanjo ◽  
Eren Erman Ozguven ◽  
Thobias Sando ◽  
Richard Twumasi-Boakye
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
The Elderly ◽  
Market Penetration ◽  
Mixed Traffic ◽  
Traffic Operations ◽  
Freeway Traffic ◽  
Elderly Drivers ◽  
Young Drivers ◽  
The Subject

Freeway merge ramps serve as one of the most challenging areas in traffic operations. This paper primarily focuses on creating a mixed traffic of conventional and connected/autonomous vehicles (CAVs) on freeways, and capturing driver behaviors both for the merging vehicle on the ramp and the freeway vehicles. The mixed distribution of vehicle headways of the freeway vehicles, developed based on various market penetration rates of the CAVs, was used to randomly generate vehicles through Monte Carlo simulation, and assigned as headways in a driving simulator. Based on perception, young drivers on the merge ramp were observed to choose critical headway gaps of 2.9 s, 1.8 s, and 1.7 s for freeway traffic of 0%, 50%, 75% penetration rates, respectively. For similar CAV penetration rates, the critical gaps observed for elderly drivers were 3.5 s, 2.0 s, and 1.9 s, respectively. When actually driving in the simulator, for the scenarios of 0% CAVs and 50% CAVs on the freeway, the values of average headway gaps accepted by young drivers were estimated as 2.36 s and 1.53 s, respectively. For the elderly drivers driving the simulator, the average headway gap values accepted were estimated as 2.72 s and 1.55 s, respectively, in the 0% and 50% penetration rates on the freeway traffic. Analyses of the speed profiles of the vehicles showed the effects of the acceleration/deceleration of merging vehicles, for both young and older drivers, on the freeway vehicles, including a few cases of collision. Overall, it was observed that the subject drivers accepted shorter headway gaps for increased CAV penetration levels.

Design and implementation of human driving data–based active lane change control for autonomous vehicles

2020 ◽  
Vol 235 (1) ◽  
pp. 55-77
Author(s):  
Heungseok Chae ◽  
Yonghwan Jeong ◽  
Hojun Lee ◽  
Jongcherl Park ◽  
Kyongsu Yi
Keyword(s):  
Real World ◽  
Autonomous Vehicles ◽  
High Speed ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lane Change ◽  
Driving Mode ◽  
Lane Changes ◽  
Safety Indices

This article describes the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles with human factor considerations. Lane changes need to be performed considering both driver acceptance and safety with surrounding vehicles. Therefore, autonomous driving systems need to be designed based on an analysis of human driving behavior. In this article, manual driving characteristics are investigated using real-world driving test data. In lane change situations, interactions with surrounding vehicles were mainly investigated. And safety indices were developed with kinematic analysis. A safety indices–based lane change decision and control algorithm has been developed. In order to improve safety, stochastic predictions of both the ego vehicle and surrounding vehicles have been conducted with consideration of sensor noise and model uncertainties. The desired driving mode is decided to cope with all lane changes on highway. To obtain desired reference and constraints, motion planning for lane changes has been designed taking stochastic prediction-based safety indices into account. A stochastic model predictive control with constraints has been adopted to determine vehicle control inputs: the steering angle and the longitudinal acceleration. The proposed active lane change algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable lane changes in high-speed driving on highways have been demonstrated using our autonomous test vehicle.

Driver Response Time in Cut-Off Scenarios from the Second Strategic Highway Research Program Naturalistic Database

2021 ◽  
pp. 036119812110453
Author(s):  
Swaroop Dinakar ◽  
Jeffrey W. Muttart ◽  
Darlene E. Edewaard ◽  
Michael Giannone ◽  
Connor Dickson
Keyword(s):  
Research Program ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Data Set ◽  
Factors Associated ◽  
Driver Response ◽  
Lane Changes ◽  
The Subject ◽  
Highway Research ◽  
Strategic Highway Research Program

A cut-in or cut-off scenario involves a vehicle intruding into the path of another vehicle traveling in the same direction. These lane changes can lead to potentially dangerous situations, either a sideswipe or a rear-end crash. In this study, 552 cut-in events were analyzed, including four crash and 548 near-crash events from the Second Strategic Highway Research Program (SHRP-2) data set. Video and onboard-data-recorder data from the responding vehicle were used to analyze various factors associated with drivers’ responses. Driver response times were measured from three different event onsets, and the effects of different factors on the respective response times were measured. These factors included the behavior of the subject driver, the behavior of the intruding vehicle/principal other vehicle (POV), and different environmental and infrastructural factors. The results showed that drivers responded more slowly when the POV took longer to move laterally to the subject driver’s lane edge and faster when this time was short. Similarly, drivers responded faster to merging vehicles that started from a stop. Yet, response times were no different when the POV utilized a directional signal. These results point to a kinematic threshold involving lateral distance and lateral speed that best describes how drivers were triggered to respond. Drivers also responded faster near intersections, and at night. The results can be utilized to design crash mitigation systems in autonomous vehicles, as well as non-automated vehicles, to supplement human responses where their abilities may be lacking.

scholarly journals Lane change maneuver for autonomous vehicles (Benchmark Proposal)

10.29007/5hxt ◽  
2018 ◽  
Author(s):  
Nikolaos Kekatos ◽  
Daniel Heß ◽  
Goran Frehse
Keyword(s):  
Autonomous Vehicles ◽  
Lane Change ◽  
Lane Changes ◽  
Cooperative Driving ◽  
Bicycle Model ◽  
Moving Vehicles ◽  
Control Scheme ◽  
Lateral Behavior ◽  
The Right ◽  
Left Lane

Lane changes are known to be risky maneuvers both for autonomous vehicles and human drivers since they require changes in longitudinal and lateral velocities in the presence of other moving vehicles. In this paper, we propose a benchmark modeling a cooperative lane change maneuver that involves four fully autonomous vehicles; three in the left lane and one in the right. The vehicle driving in the right lane aims to move to the left lane while avoiding a collision with the other vehicles. Each vehicle is equipped with sensors and can also communicate with its neighboring vehicles. The vehicle dynamics are described by a dynamic bicycle model and each vehicle is equipped with a linear low-level controller that regulates its own longitudinal and lateral behavior. To guarantee that the maneuver is safe and the traffic rules are enforced, we employ a cooperative driving control scheme (in the spirit of supervisory logic) that decides the actions of each vehicle.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

2021 ◽  
pp. 036119812098585
Author(s):  
Paolo Delle Site
Keyword(s):  
Autonomous Vehicles ◽  
Marginal Cost ◽  
User Behavior ◽  
Social Planner ◽  
Traffic Networks ◽  
Mixed Traffic ◽  
The Third ◽  
First Case ◽  
Marginal Cost Pricing ◽  
The Social

For networks with human-driven vehicles (HDVs) only, pricing with arc-specific tolls has been proposed to achieve minimization of travel times in a decentralized way. However, the policy is hardly feasible from a technical viewpoint without connectivity. Therefore, for networks with mixed traffic of HDVs and connected and autonomous vehicles (CAVs), this paper considers pricing in a scenario where only CAVs are charged. In contrast to HDVs, CAVs can be managed as individual vehicles or as a fleet. In the latter case, CAVs can be routed to minimize the travel time of the fleet of CAVs or that of the entire fleet of HDVs and CAVs. We have a selfish user behavior in the first case, a private monopolist behavior in the second, a social planner behavior in the third. Pricing achieves in a decentralized way the social planner optimum. Tolls are not unique and can take both positive and negative values. Marginal cost pricing is one solution. The valid toll set is provided, and tolls are then computed according to two schemes: one with positive tolls only and minimum toll expenditure, and one with both tolls and subsidies and zero net expenditure. Convergent algorithms are used for the mixed-behavior equilibrium (simplicial decomposition algorithm) and toll determination (cutting plane algorithm). The computational experience with three networks: a two-arc network representative of the classic town bypass case, the Nguyen-Dupuis network, and the Anaheim network, provides useful policy insight.

Sign in / Sign up

Export Citation Format

Share Document