Models of human reaction to vehicle environments

Jacobson, I. D.; Richards, Larry G.; Kuhlthau, A. R.

doi:10.1016/0003-6870(78)90010-8

Cited by 9 publications

(10 citation statements)

References 4 publications

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“…It must be noted that acceleration/deceleration (the 2nd derivative of distance with respect to time) is only one dimension of passengers' ride experience, others include vibration/oscillation (which depends in part on a vehicle's suspension) and 'jerk' (the 3rd derivative of distance with respect to time; the rate of change in acceleration/deceleration). 'Jerk' is known to be an important determinant of passenger comfort (Hoberock, 1976;Jacobsen et al, 1978; McKenzie and Brumaghin, 1976); however, unlike acceleration, 'jerk' is not user-definable in VISSIM simulation software. It is also worth noting that point values were used for the HSR and LRT constraints (i.e.…”

Section: Lrt/hsr Scenario #2: Longitudinal and Lateral Acceleration/dmentioning

confidence: 97%

“…Traveler comfort is a subjective concept, with seminal studies in the domain dating from the 1970s (Hoberock, 1976;Jacobsen et al, 1978;McKenzie and Brumaghin, 1976). Certain correlates of experienced comfort are clear: Temperature, rate of acceleration/deceleration, 'jerk' (the first derivative of acceleration), seating type, perceived personal security, crowding level, etc.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous cars: The tension between occupant experience and intersection capacity

Vine

Zolfaghari

Polak

2015

Transportation Research Part C: Emerging Technologies

202

102

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Section: Lrt/hsr Scenario #2: Longitudinal and Lateral Acceleration/dmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Autonomous cars: The tension between occupant experience and intersection capacity

Vine

Zolfaghari

Polak

2015

Transportation Research Part C: Emerging Technologies

202

102

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“…On the other hand, if the average delay is smaller than that for congested traffic conditions, a positive reward is given. Consequently, the subfunction for traffic efficiency is defined as follows: Lastly, the subfunction for driving comfort (R c ) is defined based on an existing driving comfort model (Zhu et al, 2020;Jacobson et al, 1980). In this driving comfort model, the level of driving comfort is quantified using the change rate of acceleration called Jerk as follows: R c = − jerk 2 27.04 .…”

Section: Designing the Acc Agentmentioning

confidence: 99%

SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning

Das,

Won

2021

Preprint

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We present an autonomous adaptive cruise control (ACC) system namely SAINT-ACC: Safety-Aware Intelligent ACC system (SAINT-ACC) that is designed to achieve simultaneous optimization of traffic efficiency, driving safety, and driving comfort through dynamic adaptation of the inter-vehicle gap based on deep reinforcement learning (RL). A novel dual RL agentbased approach is developed to seek and adapt the optimal balance between traffic efficiency and driving safety/comfort by effectively controlling the driving safety model parameters and inter-vehicle gap based on macroscopic and microscopic traffic information collected from dynamically changing and complex traffic environments. Results obtained through over 12,000 simulation runs with varying traffic scenarios and penetration rates demonstrate that SAINT-ACC significantly enhances traffic flow, driving safety and comport compared with the state-of-the-art approach.

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“…While those are all relevant, some are hard to quantify. We choose to work on motion comfort, which is largely influenced by the vehicle's longitudinal and lateral jerk [18,26,44]. Due to the short-term predictive nature of most end-to-end driving models, substantial jerking is an inherent problem.…”

Section: Accurate and Comfortable Drivingmentioning

confidence: 99%

Learning Accurate, Comfortable and Human-like Driving

Hecker¹,

Dai²,

Gool³

2019

Preprint

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Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but also similar to how human drivers would. This is especially true when autonomous and human-driven vehicles need to share the same road. The main research focus thus far, however, is still on improving driving accuracy only. This paper formalizes the three concerns with the aim of accurate, comfortable and human-like driving. Three contributions are made in this paper. First, numerical map data from HERE Technologies are employed for more accurate driving; a set of map features -which are believed to be relevant to driving -are engineered to navigate better. Second, the learning procedure is improved from a pointwise prediction to a sequence-based prediction and passengers' comfort measures are embedded into the learning algorithm. Finally, we take advantage of the advances in adversary learning to learn human-like driving; specifically, the standard L1 or L2 loss is augmented by an adversary loss which is based on a discriminator trained to distinguish between human driving and machine driving. Our model is trained and evaluated on the Drive360 dataset, which features 60 hours and 3000 km of real-world driving data. Extensive experiments show that our driving model is more accurate, more comfortable and behaves more like a human driver than previous methods. The resources of this work will be released on the project page.

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Models of human reaction to vehicle environments

Cited by 9 publications

References 4 publications

Autonomous cars: The tension between occupant experience and intersection capacity

Autonomous cars: The tension between occupant experience and intersection capacity

SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning

Learning Accurate, Comfortable and Human-like Driving

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