Modeling the Imperfect Driver: Incorporating Human Factors in a Microscopic Traffic Model

Lindorfer, Manuel; Mecklenbräuker, Christoph F.; Ostermayer, Gerald

doi:10.1109/tits.2017.2765694

Cited by 39 publications

(20 citation statements)

References 59 publications

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“…Moreover, drivers are not fully aware of the potential collision risks while driving ( 43 , 44 ). Their misperceptions of the driving environment and mis-estimations of the risks of collision are the direct causes of traffic accidents, and greatly affect the spacing and relative speed between two consecutive vehicles ( 45 , 46 ). Their perception errors on spacing and the relative speeds of vehicles affect the drivers’ estimations of the collision risks, and these eventually increase the collision risks of the subject vehicle and the subsequent followers.…”

Section: Evaluation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Development of Risk Predictive Collision Avoidance System and Its Impact on Traffic and Vehicular Safety

Lee

Tak

Choi

et al. 2019

Transportation Research Record: Journal of the Transportation R

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Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.

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Section: Evaluation Methodsmentioning

confidence: 99%

“…The perception error on the speed of the subject vehicle is sometimes set to zero, since the speed can be measured by the speedometer ( 45 , 46 ). Some other studies have claimed, however, that there may still be differences between the actual vehicle speed and the speed indicated by the speedometer ( 48 , 49 ).…”

Section: Evaluation Methodsmentioning

confidence: 99%

Development of Risk Predictive Collision Avoidance System and Its Impact on Traffic and Vehicular Safety

Lee

Tak

Choi

et al. 2019

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…e agent-based modelling's default driving strategy and vehicle model were customized to develop more realistic vehicle dynamics. Various modeling approaches in the literature established the spatial-temporal anticipation ability of human drivers [51][52][53][54][55][56][57].…”

Section: A Framework Of Multilane Trafficmentioning

confidence: 99%

Multilane Microscopic Modeling to Measure Mobility and Safety Consequences of Mixed Traffic in Freeway Weaving Sections

Seraj

Qiu

2021

Journal of Advanced Transportation

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Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.

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“…It is originally designed to be collision-free. However, after modifying some of the key parameters, such as the politeness, acceleration,and the grid distance estimation, the algorithm can generate adversarial behaviors for testing (Feng et al, 2021;Lindorfer et al, 2018). Other methods, including the risk field (Kolekar et al, 2020a), artificial potential field (Rasekhipour et al, 2017;Gao et al, 2019), constrained Delaunay triangulation (Huang et al, 2021b,a) , and scene prediction (Lawitzky et al, 2013), are capable of modeling human driver's cognitive states while considering safety.…”

Section: The Decision-making Behind the Scenesmentioning

confidence: 99%

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

Zhang¹,

Hang²,

Huang³

et al. 2021

Preprint

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Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email: lyuchen@ntu.edu.sg

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Modeling the Imperfect Driver: Incorporating Human Factors in a Microscopic Traffic Model

Cited by 39 publications

References 59 publications

Development of Risk Predictive Collision Avoidance System and Its Impact on Traffic and Vehicular Safety

Development of Risk Predictive Collision Avoidance System and Its Impact on Traffic and Vehicular Safety

Multilane Microscopic Modeling to Measure Mobility and Safety Consequences of Mixed Traffic in Freeway Weaving Sections

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

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