Field Testing of a Cyclist Collision Avoidance System for Heavy Goods Vehicles

Jia, Yanbo; Cebon, David

doi:10.1109/tvt.2016.2538801

Cited by 22 publications

(37 citation statements)

References 3 publications

(8 reference statements)

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“…This strategy was shown to be effective at preventing reconstructed accidents in simulation and was successfully proven in low-speed field trials on a prototype system 6 ( Figure 2).…”

Section: Cyclist Detection Systemsmentioning

confidence: 94%

Camera-based measurement of cyclist motion

Eddy

Saxe

Cebon

2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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Heavy Goods Vehicles (HGVs) are overrepresented in cyclist fatality statistics in the UK relative to their proportion of total traffic volume. In particular, the statistics highlight a problem for vehicles turning left across the path of a cyclist on their inside. In this paper we present a camera-based system to detect and track cyclists in the blind spot. The system uses boosted classifiers and geometric constraints to detect cyclist wheels, and Canny edge detection to locate the ground contact point. The locations of these points are mapped into physical coordinates using a calibration system based on the ground plane. A Kalman Filter is used to track and predict the future motion of the cyclist. Full-scale tests were conducted using a construction vehicle fitted with two cameras, and the results compared with measurements from an ultrasonic-sensor system. Errors were comparable to the ultrasonic system, with average error standard deviation of 4.3 cm when the cyclist was 1.5 m from the HGV, and 7.1 cm at a distance of 1 m. When results were compared to manually extracted cyclist position data, errors were less than 4 cm at separations of 1.5 m and 1 m. Compared to the ultrasonic system, the camera system requires simpler hardware and can easily differentiate cyclists from stationary or moving background objects such as parked cars or roadside furniture. However, the cameras suffer from reduced robustness and accuracy at close range, and cannot operate in low-light conditions.

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“…This strategy was shown to be effective at preventing reconstructed accidents in simulation and was successfully proven in low-speed field trials on a prototype system 6 ( Figure 2).…”

Section: Cyclist Detection Systemsmentioning

confidence: 94%

Camera-based measurement of cyclist motion

Eddy

Saxe

Cebon

2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Chen et al [37] predicted rear-end collisions using GA-optimized neural networks. Collisions between cyclists and heavy goods vehicles were prevented in [38] by solving the side-to-side collisions problem. However, these approaches did not consider collisions from any other directions.…”

Section: A Collision Avoidancementioning

confidence: 99%

RACE: Reinforced Cooperative Autonomous Vehicle Collision Avoidance

Yuan

Tasik

Adhatarao

et al. 2020

IEEE Trans. Veh. Technol.

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With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle's location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density.

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“…Several risk factors were identified within the reviewed non-accident studies. These related to road users are behavioural adaptation to safety measures [34], efficiency of mirrors highly depends on the truck driver's alertness [50], challenging scanning of multiple mirrors in high workload situations [55] and truck drivers' overload with physical and cognitive tasks, which affect the driving performance, particularly in turning manoeuvres [51,57]. These related to vehicle are frequent false positive alarms of an active safety system (they are annoying for truck drivers and can cause them to avoid using this system) [50], off-tracking of large trucks in turning manoeuvres (i.e.…”

Section: Risk Factorsmentioning

confidence: 99%

“…These related to vehicle are frequent false positive alarms of an active safety system (they are annoying for truck drivers and can cause them to avoid using this system) [50], off-tracking of large trucks in turning manoeuvres (i.e. the last axle is not able to follow the first axle) results in the truck encroaching on the area where cyclists travel [53,57], sound insulation of the truck cab can contribute to the reduction in drivers situational awareness around their truck [47] and a typical detection system warns only one of the two participants about each other's presence [45].…”

Section: Risk Factorsmentioning

confidence: 99%

Truck-bicycle safety: an overview of methods of study, risk factors and research needs

Pokorny¹,

Pitera²

2019

Eur. Transp. Res. Rev.

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The growing numbers of cyclists either injured or killed in accidents caused by trucks have been generally regarded as a safety problem since the 1980s (McCarthy & Gilbert, Accident Analysis & Prevention 28:275-279, 1996). Indeed, in several countries, cyclists killed by a truck represent almost 30% of all cycling fatalities (Pokorny et al., Transportation Research Procedia, 25, 2017). Whilst increasing attention has been paid to this topic by road safety researchers, a scoping review of the current research has been lacking. The aim of this paper is therefore to present a scoping review of the research literature related specifically to truck-bicycle safety, including both safety analysis and measures. Out of the 1,530 documents initially identified in the first phase of this search, 43 were selected for the final analysis. The review outlines the prevailing topics studied and research methods utilized for exploring these topics. Furthermore, findings regarding accident risk factors are summarised, as the information they provide presents us with a key for implementing more efficient safety measures. Additionally, suggestions for future research needs are identified.

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Field Testing of a Cyclist Collision Avoidance System for Heavy Goods Vehicles

Cited by 22 publications

References 3 publications

Camera-based measurement of cyclist motion

Camera-based measurement of cyclist motion

RACE: Reinforced Cooperative Autonomous Vehicle Collision Avoidance

Truck-bicycle safety: an overview of methods of study, risk factors and research needs

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