Development of Platooning Technology with Road Condition Monitoring

In this paper, we present a complete platooning system using a time-delay algorithm. The platooning is achieved by measuring the driver inputs from the lead vehicle and sending these inputs to the trail vehicle with a time-delay so that the trail vehicle can exactly mimic the motion of the lead vehicle. This system also does a road condition monitor as an add-on benefit which will help in assisting the driver of the trail vehicle/vehicle which takes the same path. The function of this monitoring system is to analyse the road surface using a lead vehicle and acquire sensor data, this acquired sensor data helps in assisting drivers who take the same track. The combination of both this platooning method and road condition monitoring system could potentially reduce the current risk of utilising this semi-automated driving system. Index terms: Platooning, Semi-automated driving, Road condition monitoring, Time-delay algorithm.

Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the future. Autonomous vehicles are supposed to provide a safe, easy-to-use and environmentally friendly means of transport. To this end, improving AVs’ safety and energy efficiency by using advanced control and optimization algorithms has become an active research topic to deliver on new commitments: carbon reduction and responsible innovation. The focus of this study is to improve the energy consumption of an AV in a vehicle-following process while safe driving is satisfied. We propose a cascade control system in which an autonomous cruise controller (ACC) is integrated with an energy management system (EMS) to reduce energy consumption. An adaptive model predictive control (AMPC) is proposed as the ACC to control the acceleration of the ego vehicle (the following vehicle) in a vehicle-following scenario, such that it can safely follow the lead vehicle in the same lane on a highway. The proposed ACC appropriately switches between speed and distance control systems to follow the lead vehicle safely and precisely. The computed acceleration is then used in the EMS component to find the optimal engine torque that minimizes the fuel consumption of the ego vehicle. EMS is designed based on two methods: type 1 fuzzy logic system (T1FLS) and interval type 2 fuzzy logic system (IT2FLS). Results show that the combination of AMPC and IT2FLS significantly reduces fuel consumption while the ego vehicle follows the lead vehicle safely and with a minimum spacing error. The proposed controller facilitates smarter energy use in AVs and supports safer transportation.

Role of Peripheral Vision in Brake Reaction Time During Safety Critical Events

The criticality of a rear end event depends on the brake reaction time (BRT) of the driver. Therefore, distracted driving poses greater threat in such events. Evidence accumulation model (EAM) that uses looming of the lead vehicle as main stimuli has shown significant success in estimating drivers’ BR Ts. It is often argued that drivers collect evidence for braking through peripheral vision, especially during off-road glances, and transition to forward. In this work, we have modeled evidence accumulation as a function of gaze eccentricity for off-road glances while approaching safety critical events. The model is tested with real world crash and near crash event data from SHRP2 naturalistic study. Our model shows that linear relation between gaze eccentricity and evidence accumulation rate during off road glances helps to improve EAM estimation in predicting BRT. We have also shown that brake-light onset does not influence EAM in presence of active looming.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics

Rear-end collisions are one of the serious traffic safety problems. These collisions occur when the following vehicle driver is inattentive or could not judge a potential rear-end collision situation. The use of rear-end collision warning systems (RECWS) may help drivers to avoid rear-end collision. The existing systems assumed constant driver reaction time for all driver population in their design and evaluation. They also ignore variations in driver characteristics, such as age and gender. The objectives of this thesis research are: (1) to develop reaction-time models that incorporate driver characteristics, (2) to develop a car-following simulation model that represents driver behaviour, and (3) to develop a rear-end collision warning system that accounts for driver characteristics and produces reliable collision warnings. In the human-factors study, four driver reaction-time models are developed for four different car-following scenarios: lead vehicle decelerating with normal deceleration rate, lead vehicle decelerating with emergency deceleration rate, lead vehicle stationary, and car-following acceleration regime. These models describe how the driver and situational factors affect reaction-time. The driver factors include age and gender, and the situational factors include speed and spacing between the following and lead vechiles. The developed car-following model assumes that drivers adjust their speeds based on information of both the lead and the back vehicles. The model also assumes that the driver reaction-time varies based on driver characteristics and kinematics. The proposed model represents driver behaviour in acceleration, deceleration, and steady state regimes of the car-following scenarios. Another unique feature of the model is that it explicitly considers information on the back vehicle. The model is calibrated and validated using vehicle tracking database. The driver reaction-time models and other kinematics constraints were integrated to develop a driver-sensitive rear-end collision warning system algorithm (RECWA). The developed car-following model is used to evaluate and validate the performance of the proposed RECWA. The results show that the proposed RECWA is functioning and producing reliable results. With further research and development, the proposed algorithm can be integrated into driving simulators or real vehicles to further evaluate and examine its benefits.

Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics

Rear-end collisions are one of the serious traffic safety problems. These collisions occur when the following vehicle driver is inattentive or could not judge a potential rear-end collision situation. The use of rear-end collision warning systems (RECWS) may help drivers to avoid rear-end collision. The existing systems assumed constant driver reaction time for all driver population in their design and evaluation. They also ignore variations in driver characteristics, such as age and gender. The objectives of this thesis research are: (1) to develop reaction-time models that incorporate driver characteristics, (2) to develop a car-following simulation model that represents driver behaviour, and (3) to develop a rear-end collision warning system that accounts for driver characteristics and produces reliable collision warnings. In the human-factors study, four driver reaction-time models are developed for four different car-following scenarios: lead vehicle decelerating with normal deceleration rate, lead vehicle decelerating with emergency deceleration rate, lead vehicle stationary, and car-following acceleration regime. These models describe how the driver and situational factors affect reaction-time. The driver factors include age and gender, and the situational factors include speed and spacing between the following and lead vechiles. The developed car-following model assumes that drivers adjust their speeds based on information of both the lead and the back vehicles. The model also assumes that the driver reaction-time varies based on driver characteristics and kinematics. The proposed model represents driver behaviour in acceleration, deceleration, and steady state regimes of the car-following scenarios. Another unique feature of the model is that it explicitly considers information on the back vehicle. The model is calibrated and validated using vehicle tracking database. The driver reaction-time models and other kinematics constraints were integrated to develop a driver-sensitive rear-end collision warning system algorithm (RECWA). The developed car-following model is used to evaluate and validate the performance of the proposed RECWA. The results show that the proposed RECWA is functioning and producing reliable results. With further research and development, the proposed algorithm can be integrated into driving simulators or real vehicles to further evaluate and examine its benefits.

Introducing a New Apparatus for Designating Two-Lane Highway Passing and No-Passing Zones

Two-lane highways constitute a large proportion of the U.S. highways. An essential component needed in the design of safe two-lane highways is the passing sight distance (PSD). Otherwise, insufficient PSDs lead to passing-related crashes and, therefore, no-passing zones ought to be marked. This research involves the development of a new apparatus of the two-vehicle method, which is used for measuring the PSD in the field. That is to replace the defunct apparatus used by the Wyoming Department of Transportation (WYDOT). To the best of the authors’ knowledge, the introduced apparatus is the most up-to-date system and addresses shortcomings of previous research. The two-vehicle method involves two successive vehicles spaced at a gap, equivalent to PSD, and both vehicles travel at the speed limit. The driver of the rear vehicle operates a switch when the lead vehicle becomes invisible because of sight obstructions, such as vegetation, signaling the beginning point of the no-passing zone. Similarly, the switch is operated when the lead vehicle returns to view to designate the endpoint of the no-passing zone. The apparatus is composed of vehicle-to-vehicle radio communication devices, global positioning system devices, the switch and computers with graphical user interfaces to record and display the data. Testing was conducted on two two-lane highway segments. As per the results, overall discrepancies between WYDOT’s no-passing zone markings and those designated by the apparatus, developed, ranged from 1% to 7%. This research lays the foundation for a future study involving the development of a cutting-edge prototype.

ANALYSIS OF VEHICLE-TYPE-SPECIFIC HEADWAYS ON TWO-LANE ROADS WITH MIXED TRAFFIC

Time headway is an important microscopic traffic flow parameter, which affects safety, level-of-service, capacity and traffic simulation. It is, therefore, important to know the specific distribution for a particular roadway and traffic condition. Further, headway between two vehicles depends on the type of lead vehicle and is influenced by its size and dynamics. Such impact is considerably high on two-lane roads with mixed traffic composed of a wide variety of vehicle types. This paper identified sixteen combinations of vehicle pairs and analysed vehicle-type-specific headways using field data. Appropriate distribution functions were fitted to field data and predictive models were used in understanding carfollowing behaviour. Observations indicate that quite often bike riders become reluctant in obeying lane discipline. However, car drivers show conservative attitude and usually, keep safe distance from the lead vehicle except the case when they follow another car. In addition, while following Non-Motorized Vehicles (NMV), most of the drivers keep reasonably safe distances. In this paper, a comparison of computed headway probabilities was also made with those obtained from more or less homogeneous traffic. It was found that values obtained in current study are high in most of the instances. This indicates risk-taking behaviour of driver population, which eventually affects safety of such roads. The present study, thus, demonstrates the need of investigating vehicle-type-specific headways under mixed traffic based on comprehensive field data.