scholarly journals Lane-deviation penalty formulation and analysis for autonomous vehicle avoidance maneuvers

2021 ◽  
pp. 095440702110079
Author(s):  
Pavel Anistratov ◽  
Björn Olofsson ◽  
Lars Nielsen
Keyword(s):  
Objective Function ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Emergency Situation ◽  
Change Scenario ◽  
Initial Speed ◽  
Objective Functions ◽  
Double Lane Change ◽  
Tire Friction

Autonomous vehicles hold promise for increased vehicle and traffic safety, and there are several developments in the field where one example is an avoidance maneuver. There it is dangerous for the vehicle to be in the opposing lane, but it is safe to drive in the original lane again after the obstacle. To capture this basic observation, a lane-deviation penalty (LDP) objective function is devised. Based on this objective function, a formulation is developed utilizing optimal all-wheel braking and steering at the limit of road–tire friction. This method is evaluated for a double lane-change scenario by computing the resulting behavior for several interesting cases, where parameters of the emergency situation such as the initial speed of the vehicle and the size and placement of the obstacle are varied, and it performs well. A comparison with maneuvers obtained by minimum-time and other lateral-penalty objective functions shows that the use of the considered penalty function decreases the time that the vehicle spends in the opposing lane.

scholarly journals Disruptive Impacts of Automated Driving Systems on the Built Environment and Land Use: An Urban Planner’s Perspective

2019 ◽  
Vol 5 (2) ◽  
pp. 24 ◽  
Author(s):  
Yigitcanlar ◽  
Wilson ◽  
Kamruzzaman
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Capital Investment ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Urban Transport ◽  
Current Status ◽  
Smart Mobility

Cities have started to restructure themselves into ‘smart cities’ to address the challenges of the 21st Century—such as climate change, sustainable development, and digital disruption. One of the major obstacles to success for a smart city is to tackle the mobility and accessibility issues via ‘smart mobility’ solutions. At the verge of the age of smart urbanism, autonomous vehicle technology is seen as an opportunity to realize the smart mobility vision of cities. However, this innovative technological advancement is also speculated to bring a major disruption in urban transport, land use, employment, parking, car ownership, infrastructure design, capital investment decisions, sustainability, mobility, and traffic safety. Despite the potential threats, urban planners and managers are not yet prepared to develop autonomous vehicle strategies for cities to deal with these threats. This is mainly due to a lack of knowledge on the social implications of autonomous capabilities and how exactly they will disrupt our cities. This viewpoint provides a snapshot of the current status of vehicle automation, the direction in which the field is moving forward, the potential impacts of systematic adoption of autonomous vehicles, and how urban planners can mitigate the built environment and land use disruption of autonomous vehicles.

scholarly journals Quick Routing in Autonomous Vehicles using Boosting Algorithms

2019 ◽  
Vol 9 (1S3) ◽  
pp. 539-542
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fuel Efficiency ◽  
Time Span ◽  
Minimum Time ◽  
Novel Technique ◽  
Boosting Algorithms

Self-driving automobiles are understandably the most attention grabbing utility of artificial intelligence. Until recently, we have just considered the prototypes of these cars in Sci-fi movies, with the whole thing else left to our imagination. But with advances in technology, this super notion has acquired a lifestyles of its own. Autonomous vehicle promises to improve traffic safety while at the same time, it must increase the fuel efficiency, reduce congestion and arrive to the destination at a minimum time span. We propose a novel technique to boost the algorithm to take the shortest path while the vehicle is in movement.

Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams

2017 ◽  
Vol 2625 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Parth Bhavsar ◽  
Plaban Das ◽  
Matthew Paugh ◽  
Kakan Dey ◽  
Mashrur Chowdhury
Keyword(s):  
Traffic Safety ◽  
Failure Probability ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Fault Tree ◽  
Communication Technologies ◽  
Transportation Infrastructure ◽  
New Combination ◽  
Motor Vehicles ◽  
Mixed Traffic

The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.

scholarly journals Securing Personal Data using Block chain in Autonomous Vehicle

2019 ◽  
Vol 8 (4S2) ◽  
pp. 760-763
Keyword(s):  
Science Fiction ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Personal Data ◽  
Testing Stage ◽  
The World ◽  
Driverless Cars ◽  
Block Chain ◽  
Manufacturing Stage

Autonomous vehicles like Driverless cars are seen only in science fiction movies but in 2019 they are becoming a veracity and reality. People all around the world are excited to watch the driverless car in reality. Complete driverless car is still at an advanced testing stage. An autonomous vehicle promises to improve traffic safety while at the same time it must not be prone to hacking. Even though the existence of the autonomous car is in reality there is a possibility of hackers to hack the vehicle and retrieve the precious data. To stop this kind of hacking we propose a block chain technique that safe guards the data that is fed to the autonomous car during the manufacturing stage and this cannot be deleted without proper permission.

scholarly journals Assessment of external interface of autonomous vehicles

2021 ◽  
Vol 61 (6) ◽  
pp. 733-739
Author(s):  
Adam Orlický ◽  
Alina Mashko ◽  
Josef Mík
Keyword(s):  
Decision Making ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Visual Cues ◽  
Autonomous Vehicle ◽  
Gaze Direction ◽  
Eye Tracker ◽  
Communication Interface ◽  
External Communication ◽  
External Interface

The paper deals with the problem of a communication interface between autonomous vehicles (AV) and pedestrians. The introduced methodology for assessing new and existing e-HMI (external HMI) contributes to traffic safety in cities. The methodology is implemented in a pilot experiment with a scenario designed in virtual reality (VR). The simulated scene represents an urban zebra crossing with an approaching autonomous vehicle. The projection is implemented with the help of a head-up display – a headset with a built-in eye tracker. The suggested methodology analyses the pedestrian’s decision making based on the visual cues – the signals displayed on the autonomous vehicle. Furthermore, the decision making is correlated to subjects’ eye behaviour, based on gaze-direction data. The method presented in this paper contributes to the safety of a vehicle-pedestrian communication of autonomous vehicles and is a part of a research that shall further contribute to the design and assessment of external communication interfaces of AV in general.

scholarly journals Robust trajectory tracking control for autonomous vehicle subject to velocity-varying and uncertain lateral disturbance

2021 ◽  
Vol 57 (1) ◽  
pp. 7-23
Author(s):  
Yuqiong Wang ◽  
Song Gao ◽  
Yuhai Wang ◽  
Pengwei Wang ◽  
Yingchao Zhou ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Lane Change ◽  
Trajectory Tracking Control ◽  
Velocity Planning ◽  
Double Lane Change ◽  
Lateral Disturbance

Autonomous vehicles are the most advanced intelligent vehicles and will play an important role in reducing traffic accidents, saving energy and reducing emission. Motion control for trajectory tracking is one of the core issues in the field of autonomous vehicle research. According to the characteristics of strong nonlinearity, uncertainty and chang-ing longitudinal velocity for autonomous vehicles at high speed steering condition, the robust trajectory tracking control is studied. Firstly, the vehicle system models are established and the novel target longitudinal velocity planning is carried out. This velocity planning method can not only ensure that the autonomous vehicle operates in a strong nonlinear coupling state in bend, but also easy to be constructed. Then, taking the lateral location deviation minimiz-ing to zero as the lateral control objective, a robust active disturbance rejection control path tracking controller is designed along with an extended state observer which can deal with the varying velocity and uncertain lateral dis-turbance effectively. Additionally, the feedforward-feedback control method is adopted to control the total tire torque, which is distributed according to the steering characteristics of the vehicle for additional yaw moment to enhance vehicle handing stability. Finally, the robustness of the proposed controller is evaluated under velocity-varying condi-tion and sudden lateral disturbance. The single-lane change maneuver and double-lane change maneuver under vary longitudinal velocity and different road adhesions are both simulated. The simulation results based on Matlab/Simulink show that the proposed controller can accurately observe the external disturbances and have good performance in trajectory tracking and handing stability. The maximum lateral error reduces by 0.18 meters compared with a vehicle that controlled by a feedback-feedforward path tracking controller in the single-lane change maneuver. The lateral deviation is still very small even in the double lane change case of abrupt curvature. It should be noted that our proposed control algorithm is simple and robust, thus provide great potential for engineering application.

scholarly journals Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles

Vehicles ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 523-541
Author(s):  
Abdullah Baz ◽  
Ping Yi ◽  
Ahmad Qurashi
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Autonomous Vehicle ◽  
Signalized Intersection ◽  
Mixed Traffic ◽  
Intersection Safety ◽  
Vehicle To Infrastructure ◽  
Intersection Control

The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this model allows an AV to exchange information with the roadside units (RSU) to support the decision making of whether an ordinary vehicle (OV) or an AV should pass the intersection first. The safety of vehicles is taken in different stages of decisions to assure collision-free intersection operations. Two different mathematical models have been developed, where model one is for an AV/AV situation and model two is when an AV meets an OV. A simulation model was developed to implement the algorithm and compare the performance of each model with the conventional traffic control at a four-legged signalized intersection and at a roundabout. Three levels of traffic volume and speed combinations were tested in the simulation. The results show significant reductions in delay for both cases; for case (I), AV/AV model, a 65% reduction compared to a roundabout and 84% compared to a four-legged signalized intersection, and for case (II), AV/OV model, the reduction is 30% and 89%, respectively.

scholarly journals Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles

2019 ◽  
Vol 8 (6) ◽  
pp. 5295-5301 ◽  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Vision System ◽  
Autonomous Vehicle ◽  
Computing Time ◽  
Weather Conditions ◽  
Network Architectures ◽  
Neural Network Architectures

This article considers modern rapid architectures of detecting neural networks, structural peculiarities of each selected neural network architectures are analyzed. Experiment is carried out on the basis of potentially dangerous situation upon autonomous vehicle movement; in the selected experimental environment a set of architectures for computer vision system of autonomous vehicle is analyzed, and traffic safety of autonomous vehicle is estimated under various weather conditions; computing time required for application of additional control and analysis algorithms is evaluated. Experimental results are analyzed aiming at development of reasonable selection of neural network architectures for object recognition required for variability of support of autonomous vehicle traffic. Conclusion about applicability of the considered neural network architectures is made for conditions of certain project.

scholarly journals Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1021
Author(s):  
Teck Kai Chan ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Intelligent Vehicles ◽  
Smart Device ◽  
Enabling Technology ◽  
Research Gaps ◽  
The Road ◽  
Driving Behaviors ◽  
On The Road

With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included. For example, a sudden obstacle while the autonomous vehicle executes the parking trajectory on the road is discussed. Several aspects of social problems, such as the liability of an accident affecting the autonomous vehicle, are described. A smart device to detect abnormal driving behaviors to prevent possible accidents is briefly discussed.

scholarly journals Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Yanting Sheng ◽  
Rui Feng ◽  
Salvatore Antonio Biancardo
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Safety ◽  
Collision Risk ◽  
Risk Modelling ◽  
Time To Collision ◽  
Additional Information ◽  
Traffic Conditions ◽  
Research Findings

Traffic safety plays a crucial role in the development of autonomous vehicles which attracts significant attention in the community. It is a challenge task to ensure autonomous vehicle safety under varied traffic environment interference, especially for airport-like closed-loop conditions. To that aim, we analyze autonomous vehicle safety at typical roadway conditions and traffic state constraints (e.g., car-following state at different speed distributions) by simulating the airport-like traffic conditions. The experimental results suggest that traffic collision risk is in a positive relationship with the speed difference and distance among adjacent vehicles. More specifically, the autonomous vehicle may collide with neighbors when the time to collision (TTC) indicator is lower than 4 s, and vice versa. The research findings can help both research community and practioners obtain additional information for improving traffic safety for autonomous vehicles.

Sign in / Sign up

Export Citation Format

Share Document