Aggressive Lane Research Articles

A Comprehensive Review of Autonomous Driving Algorithms: Tackling Adverse Weather Conditions, Unpredictable Traffic Violations, Blind Spot Monitoring, and Emergency Maneuvers

With the rapid development of autonomous driving technology, ensuring the safety and reliability of vehicles under various complex and adverse conditions has become increasingly important. Although autonomous driving algorithms perform well in regular driving scenarios, they still face significant challenges when dealing with adverse weather conditions, unpredictable traffic rule violations (such as jaywalking and aggressive lane changes), inadequate blind spot monitoring, and emergency handling. This review aims to comprehensively analyze these critical issues, systematically review current research progress and solutions, and propose further optimization suggestions. By deeply analyzing the logic of autonomous driving algorithms in these complex situations, we hope to provide strong support for enhancing the safety and reliability of autonomous driving technology. Additionally, we will comprehensively analyze the limitations of existing driving technologies and compare Advanced Driver Assistance Systems (ADASs) with Full Self-Driving (FSD) to gain a thorough understanding of the current state and future development directions of autonomous driving technology.

The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches

The growing concerns over road safety and the increasing popularity of two-wheeled vehicles highlight the need to address aggressive driving behaviors in this context. Understanding and detecting such behaviors can significantly contribute to rider safety and accident prevention. The primary aim of this research is to develop an effective method for detecting aggressive driving patterns, specifically focusing on rapid turns and lane-change maneuvers using two-wheeled vehicles. To achieve this objective, we conducted a survey to establish criteria for aggressive driving. Subsequently, we collected data through a virtual simulator, implementing staged aggressive driving scenarios. The data underwent preprocessing, feature engineering, and deep learning model training for detection. The results of this study demonstrate the successful detection of aggressive driving patterns, including rapid turns and lane changes, using sensor data. The criterion for rapid turns is specified as a significant change in sensor values within 1 s. In the CNN-LSTM model for aggressive lane changes, the precision for normal driving is 0.97, and the overall accuracy for aggressive driving is 95%. Our approach, which relies on sensor technology rather than impractical camera systems, showcases the potential for enhancing rider safety in two-wheeled vehicles. In conclusion, this research provides valuable insights into the detection of aggressive driving patterns in two-wheeled vehicles. By leveraging sensor data and innovative methods, it offers promising implications for improving rider safety and accident prevention in the future.

Pricing lane changes

Risky and aggressive lane changes on highways reduce capacity and increase the risk of collision. We propose a lane-changing pricing scheme as an effective tool to penalize those maneuvers to reduce congestion as a societal goal while aiming for safe driving conditions. In this paper, we first model driver behavior and their payoffs under a game theory framework and find optimal lane-changing strategies for individuals and their peers in multiple pairwise games. Payoffs are estimated for two primary evaluation criteria: efficiency and safety, which are quantified by incorporating driver tradeoffs. After that, the discretionary lane-changing (DLC) model is calibrated and validated by real-world vehicular trajectory data. To manipulate drivers’ DLC behaviors, two types of lane-changing tolls based on local-optimal and global-optimal rules are introduced to align individual preferences with social benefits. We find prices can close this gap and achieve ‘win-win’ results by reducing drivers’ aggressive lane changes in the congested traffic. Meanwhile, the tolls collected can be used to compensate drivers who get delayed when yielding, to encourage appropriate yielding behavior and a pseudo-revenue neutral tolling system.

Instantaneous Lane-Changing Type Aware Lane Change Prediction Based on LSTM in Mixed Traffic Scenario

With the rapid development and application of autonomous technology in vehicles, we are going to see more autonomous vehicles on the roads in a foreseeable future. While autonomous vehicles may have the advantage of reducing traffic accidents caused by human drivers’ neglect and/or fatigue, one of the challenges is how to develop autonomous driving algorithms such that autonomous vehicles can be safely deployed in a mixed traffic environment with both autonomous vehicles and human-driven vehicles. Instantaneous lane-changing type may be significantly different for human drivers, which would lead to traffic accidents with other vehicles including autonomous vehicles. In this paper, we propose a resilient algorithm for the prediction of the human driver’s lane-changing behaviors. The proposed algorithm uses a long-short term memory (LSTM) classifier to identify the conservative lane change and the aggressive lane changing and accordingly makes the accurate prediction on lane changes in the driving of vehicles by human drivers. The proposed method provides a useful addition in facilitating the design of more advanced driving algorithms for autonomous vehicles. Using the vehicle trajectory data in the NGSIM data set for a large number of simulations, the effectiveness of this method has been confirmed.

A Cellular Automata Model for Heterogeneous Traffic Flow Incorporating Micro Autonomous Vehicles

Despite the fact that significant research efforts have been made to the traffic flow theory of autonomous vehicles and manual vehicles, few existing studies have incorporated different modes of both vehicles in their analysis. In this study, we develop a cellular automata simulation model to investigate the impact of different modes of autonomous vehicles (autonomous car, autonomous bus, and autonomous micro car) and conventional vehicles (manual car, manual bus, and manual micro car) on the characteristics of traffic flow. A new type of autonomous mode, i.e., autonomous micro car, is investigated in the model to study the effects of this vehicle mode on the overall capacity of the network. Furthermore, two types of lane-changing behavior, i.e., aggressive lane changing and polite lane changing, are incorporated into the model. The results reveal that micro cars (manual and autonomous) have the potential to reduce traffic congestions and increase the capacity or flow rate (vehicles/hour) of the road. Where the average vehicle occupancy is less than 2, if autonomous micro cars are deployed alongside autonomous cars, the flow rate (vehicles/hour) can be increased significantly. The results highlight the significance of the autonomous micro cars to traffic flow, passenger occupancy, and road capacity.

RETRACTED: Study on the occlusion of the visibility window on highway exit ramps

Traffic accidents frequently occur at the nose of highway exit ramps for a variety of reasons. When attempting to take an exit, a driver’s line of sight may be obscured by the curvature or slope of the main expressway, roadside vegetation, traffic signs, and other vehicles traveling alongside. These increase the likelihood of accidents due to inaccurate or poor driver judgement when approaching a highway exit. Because of emotional responses that accompany the possibility of missing an exit, many drivers act irrationally through haphazard acceleration or braking, aggressive lane changes; in some cases, even coming to a complete stop and reversing against the flow of high speed traffic to correct their course. This study applies to curved highway exit corridors with a poor line of sight. The total visible area of the exit ramp is defined in accordance with the design specifications of both the highway routes and exit ramps, which was then further refined by considering the driving characteristics of both the drivers and of surrounding traffic. This study set out to establish a reliable occlusion model of highway exits with the following goals in mind. Geometric equations describing the area and volume of the target area of the exit ramp are established. Discrete expression equation was deployed to establish the occlusion model for the target area within the visual field. Parameters taken from specifications of the Hunan section of the Shanghai-Kunming Expressway are used for multiple calculations, in which obstruction patterns of objects with the same width, and objects with different width at different positions are analyzed. Based on the patterns observed from stimulated experiments mentioned above, multiple effective measures were proposed to prevent the obstruction of the highway exit ramp. In summary, this study provides theoretical support for prevention of obstruction as well as an anti-occlusion model for better identification of the highway exit ramps by intelligent vehicle systems.

Collision Imminent Steering at High Speeds on Curved Roads Using One-Level Nonlinear Model Predictive Control

Collision imminent steering is an automotive active safety feature designed to perform an evasive lane change maneuver when a forward collision cannot be avoided by braking alone. Previous work developed a nonlinear one-level model predictive controller to perform such a maneuver in a straight highway environment and at high speeds. In this article, a new formulation is presented that extends this capability to curved roads. The formulation includes a drivable tube concept, within which the maneuver is allowed to push the vehicle to its limits of handling, but only if the controller deems it necessary. Numerical simulation results showcase aggressive lane change maneuvers on curved roads at high speeds, allowing for both inside and outside lane changes, as well as single and double lane change maneuvers in a shorter distance than braking.

Collision Imminent Steering at High Speed Using Nonlinear Model Predictive Control

Collision imminent steering is an automotive active safety feature designed to perform an aggressive lane change to avoid a forward collision in the event that the system determines there is insufficient distance to avoid a collision by braking alone.In this paper, a collision imminent steering system is developed using nonlinear model predictive control to perform a lane change at high speed in a highway environment. Two formulations are presented: one formulation to show the theoretical minimum lane change distance within which a safe lane change can be performed, and a second formulation to find the minimum aggressive maneuver for a given lane change distance. The nonlinear model predictive control formulation is posed as a constrained optimization problem, where solutions obey hard safety constraints of the highway environment. Constraint aggregation techniques are analyzed and introduced to monitor the peak tire slip throughout the maneuver. Numerical simulations of a typical luxury sedan in a highway setting show that minimum distance required to change lanes is about half the limit braking distance. Hence, there is a window of feasibility where the vehicle cannot avoid collision by braking alone, but can change lanes safely and maintain a safe trajectory throughout the maneuver. Additional simulations show that an active four wheel steering architecture improves performance over front only steering.

Simulation Study of Autonomous Vehicles’ Effect on Traffic Flow Characteristics including Autonomous Buses

Autonomous vehicles are expected to revolutionize the transportation industry. The goal of this research is to study the heterogeneity in traffic flow dynamics by comparing different penetration rates of four different types of vehicles: autonomous cars (AC), autonomous buses (AB), manual cars (MC), and manual buses (MB). For the purpose of this research, a modified cellular automata (CA) model is developed in order to analyze the effect of heterogeneous vehicles (manual and autonomous). Previously, studies have focused on manual and autonomous cars, but we believe a gap in perception and analysis of mixed traffic still exists, as inclusion of other modes of autonomous vehicle research is very limited. Therefore, we have explicitly examined the effect of the AB on overall traffic flow. Moreover, two types of lane changing behavior (aggressive lane changing and polite lane changing) were also integrated into the model. Multiple scenarios through different compositions of vehicles were simulated. As per the results, if AB is employed concurrently with AC, there will be a significant improvement in traffic flow and road capacity, as equally more passengers can be accommodated in AB as AC is also anticipated to be used in carpooling. Secondly, when the vehicles change the lanes aggressively, there is a substantial growth in the flow rate and capacity of the network. Polite lane change does not significantly affect the flow rate.

Tolling roads to improve reliability

A significant cost of traffic congestion is unreliable travel times. A major source of this unreliability is that when roads are congested, interactions between drivers can lead to capacity unexpectedly falling. For example, collisions can close lanes and aggressive lane changers can slow traffic. This paper analyzes how tolls should be set when accounting for such endogenous reliability. We find tolls should be higher and maximum flow lower than we might naïvely expect; and that such tolls make homogeneous drivers better off, even before the toll revenue is used. Simulations suggest the socially optimal maximum departure rate is 15% below that which maximizes expected throughput, and that tolling reduces private costs by almost 10%.

Impact of curbside bus stop locations on mixed traffic dynamics: a bus route perspective

Intermittent lane blockage at curb-side bus stops, characterized by aggressive lane changes, undue traffic delay and drop of road capacity, creates the major bottleneck in mixed urban traffic. This paper develops an extended link-node structured two-lane cell transmission model (CTM) that is capable of dynamically capturing the effect of lane blockage at bus stops and comprehensively modelling mandatory and discretionary lane changing (MLC & DLC) manoeuvers along a bus route under various traffic demand levels. Such modelling features, with proper calibration, have been validated to outperform the traditional CTM in terms of replicating the impact of curbside bus stops on mixed traffic dynamics. Application of the proposed model at a route level indicates existence of the optimal distribution of bus stop locations that minimizes the total traffic delay regardless of link lengths and stop densities on the route. Microscopic traffic flow characteristics such as time-varying DLC, MLC and cell densities along the bus route further demonstrate the capability of the proposed route-level model to capture the traffic phenomena (e.g. turning traffic and spill-over exacerbated by stops very close to upstream intersections) that are underrated in traditional single link or intersection based studies. The proposed model, embedded with a mathematical optimization framework, may provide insights in better planning, design, and relocation of curbside bus stops along urban streets to mitigate traffic congestion and to improve bus service.

Passenger head kinematics in abrupt braking and lane change events

Objective: A test track study was conducted to quantify patterns of adult front seat passenger head motion during abrupt vehicle maneuvers.Method: Eighty-seven men and women with a wide range of body sizes and ages participated in data collection on a closed test track in a passenger sedan under manual control by a test driver. Because a primary goal of the study was to gather “unaware” data, the participants were instructed that the study was concerned with vehicle dynamics and they were required to read from a questionnaire taped to the top of their thighs as the drive began. The first event was a hard brake (approximately 1 g) to a stop from 35 mph (56 kph). Within the space of approximately 5 min the participants also experienced an aggressive lane change, a sharp right turn with simultaneous hard braking, and a second hard braking event. A Microsoft Kinect v2 sensor was positioned to view the area around the front passenger seat. Head location was tracked using the Kinect data with a novel methodology that fit 3D head scan data to the depth data acquired in the vehicle.Result: The mean (standard deviation) forward excursion of the estimated head center of gravity (CG) location in the first braking event was 135 (62) mm. The forward head CG excursion in the second braking event of 115 (51) mm was significantly less than that in the first, but the difference was small relative to the within-condition variance. Head excursion on the second braking trial was less than that on the first trial for 69% of participants. The mean maximum inboard head excursion in lane-change maneuvers was 118 (40) mm. Forward head excursions in braking were significantly smaller for older passengers and those with higher body mass index, but the combined factors accounted for less than 25% of the variance. Inboard head excursion in the lane-change event was significantly related to stature, but only about 7% of variance was related to body size. Head excursions for men and women did not differ significantly after accounting for body size.Discussion: This is the first quantitative occupant dynamics study to use a large, diverse sample of passengers, enabling the exploration of the effects of covariates such as age and body size.Conclusions: The data demonstrate that a relatively large range of head positions can be expected to result from abrupt vehicle maneuvers. The data do not support simple scaling of excursions based on body size.

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

The technology of autonomous vehicles is expected to revolutionize the operation of road transport systems. The penetration rate of autonomous vehicles will be low at the early stage of their deployment. It is a challenge to explore the effects of autonomous vehicles and their penetration on heterogeneous traffic flow dynamics. This paper aims to investigate this issue. An improved cellular automaton was employed as the modeling platform for our study. In particular, two sets of rules for lane changing were designed to address mild and aggressive lane changing behavior. With extensive simulation studies, we obtained some promising results. First, the introduction of autonomous vehicles to road traffic could considerably improve traffic flow, particularly the road capacity and free-flow speed. And the level of improvement increases with the penetration rate. Second, the lane-changing frequency between neighboring lanes evolves with traffic density along a fundamental-diagram-like curve. Third, the impacts of autonomous vehicles on the collective traffic flow characteristics are mainly related to their smart maneuvers in lane changing and car following, and it seems that the car-following impact is more pronounced.

Indiana Lane Merge System for Work Zones on Rural Freeways

The Indiana Department of Transportation and Purdue University have been investigating and implementing a novel traffic control system at two-lane freeway work zone entries to reduce the number of aggressive lane changes close to the lane taper. The system creates a variable no passing zone through a series of dynamic DO NOT PASS signs activated in response to congestion detected on the continuous lane. The Purdue team investigated the operational effect of the system to develop preliminary guidelines for system deployment. The research results indicate that the system improves drivers' behavior toward more safety and reduces travel time in the continuous lane. The simulation model developed at Purdue was used to generate data and formulate rules for system deployment. Language: en