Autonomous overtaking in highways: A receding horizon trajectory generator with embedded safety feature

Coşkun, Serdar

doi:10.1016/j.jestch.2021.02.005

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…Among them, overtaking assistance models have considerable effect on preventing accidents due to blind spot areas in accidents and driver's negligence. Considering the overtaking behavior several researchers studied the autonomous overtaking, 12 crash avoidance and overtaking advise system, 13 intelligent advice system, 14 and advanced driver assistance strategies. 15 In all these studies the main focus is on behaviors of drivers, obstacle avoidance, collision avoidance, overtaking advice, lane changing assistance, and creating safe traffic environment.…”

Section: Related Workmentioning

confidence: 99%

Overtaking Assistance System based on Signal Strength to Prevent Accidents in V2V Communication Environment

Rai,

Rodrigo,

Samadhi

et al. 2023

ECS J. Solid State Sci. Technol.

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Directional antennas with received signal strength indicator (RSSI) output from 0.52 to 1.34 V are designed as a receiving component. These antennas have the ability to rotate up to 180° to find the maximum signal strength angle. RSSI signal strength is converted as customized measurement unit as analog reading units (ARU) which varies from 0 to 400 ARU and signal receiver has -90 dBm receiving sensitivity. Experimental results demonstrate that the variance of RSSI with speed of a vehicle shows a clear relationship at the position of signal receiving point. Two regions are defined as the effective region and the non-effective region to show the capability of the proposed model. The effective region has a 60 m range from one vehicle to another, which plays a role in the overtaking process. When a vehicle is detected within the range, the RSSI value is greater than 150 ARU. When the distance is less than 160 m, the RSSI value will be greater than 200 ARU. Results prove that the overtaking status of a vehicle is clearly identified as zones and sectors ranging from 1 to 160 m.

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Section: Related Workmentioning

confidence: 99%

Overtaking Assistance System based on Signal Strength to Prevent Accidents in V2V Communication Environment

Rai,

Rodrigo,

Samadhi

et al. 2023

ECS J. Solid State Sci. Technol.

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“…Overtaking is a crucial part of autonomous driving [1]. An overtaking starts with changing to the left lane and ends up with the overtaking vehicle's lateral driving direction returning to the right lane and its left rear wheel crossing the median line [2].…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered optimisation of overtaking decision‐making strategy for autonomous driving on highway

Huang

Zhang

Chen

et al. 2022

IET Intelligent Trans Sys

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Appropriate overtaking can improve the safety and efficiency of driving. Practically, overtaking consists of two asymmetric lane-changes: firstly to the left lane and then return back. Existing literatures treated the two lane-changes as the same behaviour, which may result in frequent and unnecessary lane-changes. For the more complex first lane-change, discretionary lane-change strategies have been developed to gain speed advantage with least deceleration. Most of them, however, described vehicles' dynamics with rough simplifications different from actual execution (e.g. constant speed and constant acceleration), which may fail them in practical applications. This paper presents an event-triggered overtaking decision-making strategy for autonomous vehicles to make the overtaking meet the driver's expectations. Firstly, the difference between the left-lane change and right-lane change is analysed. Left-lane change is mainly concerned with the efficiency, whereas driving safety is the main target of the right-lane change. Secondly, an event-triggering condition covering the speed difference between the ego vehicle and its preceding vehicle is set. Afterwards, based on quantified speed advantage, considering surrounding vehicles' changes in both speed and acceleration, an optimisation of overtaking decision-making strategy is proposed. During right-lane change in an overtaking, safety is ensured by predicting whether the ego vehicle will collide with surrounding vehicles. Finally, several scenarios are defined in the combination of SCANeR studio and Simulink, which are used to test and demonstrate the effectiveness and applicability of the overtaking decision-making strategy. The results show that the proposed overtaking decision-making strategy, by mathematical quantification of speed advantage, performs well in measuring the motivation of lane-change and avoiding frequent lane changes, which correspond to driver habits.

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“…This is confirmed by statistics published in the Road safety annual report 2020 [23]: over the past 5 years, the number of passenger injuries has been reduced by almost 40%. Such Safety Driver Assistance Systems include, for example, an electronic engine management system, a remote vehicle diagnostics system, safety systems, vehicles' positioning and a number of others [8]. One of the important areas of vehicle intellectualization is accident prevention systems based on "machine vision".…”

Section: Introductionmentioning

confidence: 99%

Machine Vision in Autonomous Vehicles: Designing and Testing the Decision Making Algorithm Based on Entity Attribute Value Model

Shubenkova

Żabiński

2021

The Archives of Automotive Engineering – Archiwum Motoryzacji

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If we speak about the Smart City’s transport system, autonomous vehicles idea is the first thing that comes to mind. Today, it is strongly believed that the autonomous vehicles’ introduction into the traffic will increase the road safety. However, driverless cars are not the solution by itself. The road safety and, accordingly, sustainability will strongly depend on decision making algorithms inbuilt into the control module. Therefore, the goal of our research is to design and test the data mining algorithm based on Entity–Attribute–Value (EAV) model for decision making in the Intelligent System in the fully- or semi-autonomous vehicles. In this article, we describe the methodology to create 3 main modules of the designed Intelligent System: (1) an Object detection module; (2) a Data analysis module; (3) a Knowledge database built on decision rules generated with the help of our data mining algorithm. To build the Decision Table on the base of the real data, we have tested our algorithm on a simple collection of photos from a Polish two-lane road. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, our decision making algorithm thanks to excluding the mistakes made on the object detection stage, works faster than existing ones with the same level of correctness.

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Autonomous overtaking in highways: A receding horizon trajectory generator with embedded safety feature

Cited by 12 publications

References 33 publications

Overtaking Assistance System based on Signal Strength to Prevent Accidents in V2V Communication Environment

Overtaking Assistance System based on Signal Strength to Prevent Accidents in V2V Communication Environment

Event‐triggered optimisation of overtaking decision‐making strategy for autonomous driving on highway

Machine Vision in Autonomous Vehicles: Designing and Testing the Decision Making Algorithm Based on Entity Attribute Value Model

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