Collision Avoidance: A Literature Review on Threat-Assessment Techniques

Dahl, John; Campos, Gabriel Rodrigues de; Olsson, Carl; Fredriksson, Jonas

doi:10.1109/tiv.2018.2886682

Cited by 103 publications

(46 citation statements)

References 78 publications

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“…As a result, the European new car assessment program (Euro NCAP) now assesses AEB system functionality when the car is on a collision course with a crossing cyclist [2]. The performance of the threat-assessment algorithms implemented in safety systems (independent of their assessment domain) depends on accurately predicting drivers' intent [3]. Unfortunately, very few driver behaviour models address crossing-cyclist scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

2020

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The number of cyclists fatally struck when crossing a driver's travel path at an unsignalised intersection has been stable in recent years, indicating that more effort should be made to improve safety in this specific conflict scenario. The most recent safety systems help drivers avoid collisions with cyclists, but improving cyclist safety further requires resolving challenges unique to bicycles and cyclists. In this paper we propose a predictive computational model of driver behaviour in the intersection scenario. Although a handful of studies have focused on describing driver behaviour in this scenario, no computational model that can predict driver control can be found in the literature. The proposed model is based on a biofidelic human sensorimotor-control modelling framework. Two visual cues were used: 1) optical longitudinal looming and 2) projected post-encroachment time between the bicycle and the car. The model was optimised using data from a test-track study in which participants were asked to drive through an intersection where a cyclist would cross their travel path. The performances of the model were evaluated by comparing the simulated driver-control process with the observed control behaviour for each trial using a leave-one-out cross-validation process. The results show that the model performed rather well, reproducing braking controls and kinematics that were similar to the observations. The extent to which the model could be used by safety systems' threatassessment algorithms is discussed. Future research to improve the model's performances is suggested.

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Section: Introductionmentioning

confidence: 99%

A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

2020

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“…We solely focus on motion prediction of other traffic participants [24]- [26], which is an integral part of motion planning [27]- [29] and risk assessment [24], [30]. The following related aspects are beyond the scope of this paper: extracting the information of surrounding traffic participants from sensor measurements [31]- [33], the uncertainty of these measurements [34]- [36], and implications on the prediction for connected vehicles [37], [38].…”

Section: A Related Workmentioning

confidence: 99%

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

Koschi

Althoff

2021

IEEE Trans. Intell. Veh.

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Provably safe motion planning for automated road vehicles must ensure that planned motions do not result in a collision with other traffic participants. This is a major challenge in autonomous driving, since the future behavior of other traffic participants is not known and since traffic participants are often hidden due to occlusions. In this work, we propose a formal setbased prediction that contains all acceptable future behaviors of both detected and potentially hidden traffic participants. Based on formalized traffic rules and nondeterministic motion models, we perform reachability analysis to predict the set of possible occupancies and velocities of vehicles, pedestrians, and cyclists. Real-world experiments with a test vehicle in various traffic situations demonstrate the applicability and real-time capability of our over-approximative prediction for both online verification and fail-safe trajectory planning. Even in congested, complex traffic scenarios, our forecasting approach enables self-driving vehicles to never cause accidents.

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“…Some articles review specific behaviours, such as car-following behaviour (Saifuzzaman & Zheng, 2014), lane changing behaviour (Koesdwiady et al, 2016), and intersection behaviour (Shirazi & Morris, 2016), while others review specific driver traits such as impulsiveness (Bıçaksız & Özkan, 2016), sensation seeking (Zhang et al, 2019), and aggressive driving behaviour (Alkinani, Khan & Arshad, 2020). Few articles review the use of machine learning-based technology in ITS Nguyen et al (2018), Martinez et al (2017), Alsrehin, Klaib & Magableh (2019) and Pamuła (2016), the evolution of vehicles' sensing technology and their effect on safety (Massaro et al, 2016), and collision avoidance in assistance systems for intelligent vehicles Dahl et al (2018), Zhao et al (2017) and Mukhtar, Xia & Tang (2015). The effect of policies on driver behaviour and safety has been reviewed in Shinar & Gurion (2019).…”

Section: Survey Analysismentioning

confidence: 99%

A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

Al-Hussein

Kiah

Por

et al. 2021

PeerJ Computer Science

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In the plan and development of Intelligent Transportation Systems (ITS), understanding drivers behaviour is considered highly valuable. Reckless driving, incompetent preventive measures, and the reliance on slow and incompetent assistance systems are attributed to the increasing rates of traffic accidents. This survey aims to review and scrutinize the literature related to sensor-based driver behaviour domain and to answer questions that are not covered so far by existing reviews. It covers the factors that are required in improving the understanding of various appropriate characteristics of this domain and outlines the common incentives, open confrontations, and imminent commendations from former researchers. Systematic scanning of the literature, from January 2014 to December 2020, mainly from four main databases, namely, IEEEXplore, ScienceDirect, Scopus and Web of Science to locate highly credible peer-reviewed articles. Amongst the 5,962 articles found, a total of 83 articles are selected based on the author’s predefined inclusion and exclusion criteria. Then, a taxonomy of existing literature is presented to recognize the various aspects of this relevant research area. Common issues, motivations, and recommendations of previous studies are identified and discussed. Moreover, substantial analysis is performed to identify gaps and weaknesses in current literature and guide future researchers into planning their experiments appropriately. Finally, future directions are provided for researchers interested in driver profiling and recognition. This survey is expected to aid in emphasizing existing research prospects and create further research directions in the near future.

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Collision Avoidance: A Literature Review on Threat-Assessment Techniques

Cited by 103 publications

References 78 publications

A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

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