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CURATOR
A pinboard by
The Sparrho Team

Who can ignore the fact that self-driving cars will be on our roads, whether we like it or not?

PINBOARD SUMMARY

It's not just about the autopilot: new discoveries from engineering, psychology, maths and more

185 ITEMS PINNED

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection

Abstract: Publication date: Available online 10 December 2016 Source:Neural Networks Author(s): Jihun Kim, Jonghong Kim, Gil-Jin Jang, Minho Lee Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance.

Pub.: 17 Dec '16, Pinned: 13 Jan '17

Distributed receding horizon control for fuel-efficient and safe vehicle platooning

Abstract: Abstract This paper investigates the problem of fuel-efficient and safe control of autonomous vehicle platoons. We present a two-part hierarchical control method that can guarantee platoon stability with minimal fuel consumption. The first part vehicle controller is derived in the context of receding horizon optimal control by constructing and solving an optimization problem of overall fuel consumption. The Second part platoon controller is a complementation of the first part, which is given on the basis of platoon stability analysis. The effectiveness of the presented platoon control method is demonstrated by both numerical simulations and experiments with laboratory-scale Arduino cars.AbstractThis paper investigates the problem of fuel-efficient and safe control of autonomous vehicle platoons. We present a two-part hierarchical control method that can guarantee platoon stability with minimal fuel consumption. The first part vehicle controller is derived in the context of receding horizon optimal control by constructing and solving an optimization problem of overall fuel consumption. The Second part platoon controller is a complementation of the first part, which is given on the basis of platoon stability analysis. The effectiveness of the presented platoon control method is demonstrated by both numerical simulations and experiments with laboratory-scale Arduino cars.

Pub.: 17 Nov '16, Pinned: 15 Dec '16

Influence of speed-related auditory feedback on braking in a 3D-driving simulator

Abstract: Publication date: January 2017 Source:Transportation Research Part F: Traffic Psychology and Behaviour, Volume 44 Author(s): Lionel Bringoux, Jocelyn Monnoyer, Patricia Besson, Christophe Bourdin, Sébastien Denjean, Erick Dousset, Cédric Goulon, Richard Kronland-Martinet, Pierre Mallet, Tanguy Marqueste, Cécile Martha, Vincent Roussarie, Jean-François Sciabica, Anca Stratulat Although discrete auditory stimuli have been found useful for emergency braking, the role of continuous speed-related auditory feedback has not been investigated yet. This point may though be of importance in electric vehicles in which acoustic cues are drastically changed. The present study addressed this question through two experiments. In experiment 1, 12 usual drivers were exposed to naturalistic auditory feedback mimicking those issued from electric cars, while facing dynamic visual scenes in a 3D driving simulator. After being passively travelled up to a sustained constant speed, subjects had to stop their car in front of a traffic light that unexpectedly turned to red. Modifications of the speed-related auditory feedback did not impact braking initiation and regulation. In experiment 2, synthesized auditory feedback based on the Shepard-Risset glissando was provided to a new sample of 15 usual drivers in the same task. Pitch variations of this acoustic stimulus, although not scaled to an absolute speed, were manipulated as a function of visual speed changes. Changing the mapping between pitch variations of the synthesized auditory feedback and visual speed changes induced adjustments on braking which depended on acceleration/deceleration feedback. These findings stressed the importance of the acoustic content and its dynamics for car speed control.

Pub.: 20 Nov '16, Pinned: 15 Dec '16

Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates.

Abstract: The objective of this study was to evaluate the effectiveness of forward collision warning (FCW) alone, a low-speed autonomous emergency braking (AEB) system operational at speeds up to 19mph that does not warn the driver prior to braking, and FCW with AEB that operates at higher speeds in reducing front-to-rear crashes and injuries. Poisson regression was used to compare rates of police-reported crash involvements per insured vehicle year in 22 U.S. states during 2010-2014 between passenger vehicle models with FCW alone or with AEB and the same models where the optional systems were not purchased, controlling for other factors affecting crash risk. Similar analyses compared rates between Volvo 2011-2012 model S60 and 2010-2012 model XC60 vehicles with a standard low-speed AEB system to those of other luxury midsize cars and SUVs, respectively, without the system. FCW alone, low-speed AEB, and FCW with AEB reduced rear-end striking crash involvement rates by 27%, 43%, and 50%, respectively. Rates of rear-end striking crash involvements with injuries were reduced by 20%, 45%, and 56%, respectively, by FCW alone, low-speed AEB, and FCW with AEB, and rates of rear-end striking crash involvements with third-party injuries were reduced by 18%, 44%, and 59%, respectively. Reductions in rear-end striking crashes with third-party injuries were marginally significant for FCW alone, and all other reductions were statistically significant. FCW alone and low-speed AEB reduced rates of being rear struck in rear-end crashes by 13% and 12%, respectively, but FCW with AEB increased rates of rear-end struck crash involvements by 20%. Almost 1 million U.S. police-reported rear-end crashes in 2014 and more than 400,000 injuries in such crashes could have been prevented if all vehicles were equipped with FCW and AEB that perform similarly as systems did for study vehicles.

Pub.: 30 Nov '16, Pinned: 30 Nov '16

Nonlinear time-frequency control of PM synchronous motor instability applicable to electric vehicle application

Abstract: Of the many technologies being explored to address sustainability and environmental issues, electric cars are considered to be the most promising alternative to vehicles powered by IC engines. This paper studies the instability control of electric vehicles propelled by permanent magnet synchronous motors (PMSMs). The nonlinear characteristics of a surface-mounted PMSM model are studied under three different assumed driving conditions. To mitigate undesirable dynamic instabilities including hyperchaotic responses that are frequented at low and high speeds, so as to extend the operating range of the PMSM system, a novel control scheme that exerts simultaneous control in both the time and frequency domains is developed and subsequently validated. The control approach has its foundation established in discrete wavelet transformation and adaptive control. Its physical implementation consists of an adaptive controller and an adaptive filter both implemented in the wavelet domain. Numerical results demonstrate the effectiveness of the controller design in restoring PMSM instability with low-amplitude limit-cycle in response to a properly specified reference signal. Of the many technologies being explored to address sustainability and environmental issues, electric cars are considered to be the most promising alternative to vehicles powered by IC engines. This paper studies the instability control of electric vehicles propelled by permanent magnet synchronous motors (PMSMs). The nonlinear characteristics of a surface-mounted PMSM model are studied under three different assumed driving conditions. To mitigate undesirable dynamic instabilities including hyperchaotic responses that are frequented at low and high speeds, so as to extend the operating range of the PMSM system, a novel control scheme that exerts simultaneous control in both the time and frequency domains is developed and subsequently validated. The control approach has its foundation established in discrete wavelet transformation and adaptive control. Its physical implementation consists of an adaptive controller and an adaptive filter both implemented in the wavelet domain. Numerical results demonstrate the effectiveness of the controller design in restoring PMSM instability with low-amplitude limit-cycle in response to a properly specified reference signal.

Pub.: 01 Dec '16, Pinned: 28 Nov '16

Effective pedestrian detection using deformable part model based on human model

Abstract: Abstract Recently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.AbstractRecently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.

Pub.: 25 Oct '16, Pinned: 20 Nov '16

Human factors of transitions in automated driving: A general framework and literature survey

Abstract: Publication date: November 2016 Source:Transportation Research Part F: Traffic Psychology and Behaviour, Volume 43 Author(s): Zhenji Lu, Riender Happee, Christopher D.D. Cabrall, Miltos Kyriakidis, Joost C.F. de Winter The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control, longitudinal control, and monitoring) between the driver and the automation. A transition in automated driving is defined as the process during which the human-automation system changes from one driving state to another, with transitions of monitoring activity and transitions of control being among the possibilities. Based on ‘Is the transition required?’, ‘Who initiates the transition?’, and ‘Who is in control after the transition?’, we define six types of control transitions between the driver and automation: (1) Optional Driver-Initiated Driver-in-Control, (2) Mandatory Driver-Initiated Driver-in-Control, (3) Optional Driver-Initiated Automation-in-Control, (4) Mandatory Driver-Initiated Automation-in-Control, (5) Automation-Initiated Driver-in-Control, and (6) Automation-Initiated Automation-in-Control. Use cases per transition type are introduced. Finally, we interpret previous experimental studies on transitions using our framework and identify areas for future research. We conclude that our framework of driving states and transitions is an important complement to the levels of automation proposed by transportation agencies, because it describes what the driver and automation are doing, rather than should be doing, at a moment of time.

Pub.: 07 Nov '16, Pinned: 20 Nov '16

Individually constructed criteria for perception of urban transportation means – An approach based on Kelly’s personal construct theory

Abstract: Publication date: January 2017 Source:Transportation Research Part F: Traffic Psychology and Behaviour, Volume 44 Author(s): Ines Kawgan-Kagan, Stephan Daubitz Understanding of acceptance of electric mobility has been typically discussed by a comparison of vehicles with different types of propulsion engines, battery electric vehicles and vehicles with an internal combustion engine. Nevertheless, electric mobility comprehends a combination of public transport and electric vehicles. The aim of this paper is to understand peoples’ outlook on electric mobility by identifying shared aspects of the assessment of battery electric vehicles and different user perspectives on transportation. A special research design in the form of repertory grids provides an opportunity to study the underlying causes of the cognitive perceptions and emotions relating to electric mobility. Cognitive interviews motivate respondents to reflect beyond the insights provided by standard forms of interview. Especially for the topic of battery electric vehicles, prejudices - for instance, those propagated by the media - are discarded and the actual requirements and patterns of mobility become visible. The special tasks involved in the interviews lead, for example, to deliberation on how to integrate battery charging processes into existing mobility patterns. This special method reveals that individuals take an interest in more characteristics of modes of transport than those that are usually analysed when researching electric mobility. In addition, three anticipation clusters can be identified for individuals with a higher affinity for cars. First, the perception of battery electric vehicles shows high levels of similarity to cars with internal combustion engines and that differentiating between types of engines is meaningless. Second, battery electric vehicles are perceived as a part of urban public transport. Third, battery electric vehicles are viewed as similar to pedelecs and segways, whereas questions of range, innovation and environmental aspects play a greater role in perceptions. These results lead to the conclusion that when studying the acceptance of BEVs, a comparison between cars with internal combustion engines and battery electric vehicles is not sufficient to grasp the complete user perspective. An analysis within the framework of a wider range of modes of transport is required in order to address people’s transportation needs.

Pub.: 13 Nov '16, Pinned: 20 Nov '16

Short-term speed predictions exploiting big data on large urban road networks

Abstract: Publication date: December 2016 Source:Transportation Research Part C: Emerging Technologies, Volume 73 Author(s): Gaetano Fusco, Chiara Colombaroni, Natalia Isaenko Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.

Pub.: 11 Nov '16, Pinned: 15 Nov '16

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Abstract: Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too defensive so that normal traffic flow is maintained. In this paper we apply deep reinforcement learning to the problem of forming long term driving strategies. We note that there are two major challenges that make autonomous driving different from other robotic tasks. First, is the necessity for ensuring functional safety - something that machine learning has difficulty with given that performance is optimized at the level of an expectation over many instances. Second, the Markov Decision Process model often used in robotics is problematic in our case because of unpredictable behavior of other agents in this multi-agent scenario. We make three contributions in our work. First, we show how policy gradient iterations can be used without Markovian assumptions. Second, we decompose the problem into a composition of a Policy for Desires (which is to be learned) and trajectory planning with hard constraints (which is not learned). The goal of Desires is to enable comfort of driving, while hard constraints guarantees the safety of driving. Third, we introduce a hierarchical temporal abstraction we call an "Option Graph" with a gating mechanism that significantly reduces the effective horizon and thereby reducing the variance of the gradient estimation even further.

Pub.: 11 Oct '16, Pinned: 10 Nov '16

Meeting points in ridesharing: A privacy-preserving approach

Abstract: Publication date: November 2016 Source:Transportation Research Part C: Emerging Technologies, Volume 72 Author(s): Ulrich Matchi Aïvodji, Sébastien Gambs, Marie-José Huguet, Marc-Olivier Killijian Nowadays, problems of congestion in urban areas due to the massive usage of cars, last-minute travel needs and progress in information and communication technologies have fostered the rise of new transportation modes such as ridesharing. In a ridesharing service, a car owner shares empty seats of his car with other travelers. Recent ridesharing approaches help to identify interesting meeting points to improve the efficiency of the ridesharing service (i.e., the best pick-up and drop-off points so that the travel cost is competitive for both driver and rider). In particular, ridesharing services, such as Blablacar or Carma, have become a good mobility alternative for users in their daily life. However, this success has come at the cost of user privacy. Indeed in current’s ridesharing services, users are not in control of their own data and have to trust the ridesharing operators with the management of their data. In this paper, we aim at developing a privacy-preserving service to compute meeting points in ridesharing, such that each user remains in control of his location data. More precisely, we propose a decentralized architecture that provides strong security and privacy guarantees without sacrificing the usability of ridesharing services. In particular, our approach protects the privacy of location data of users. Following the privacy-by-design principle, we have integrated existing privacy enhancing technologies and multimodal shortest path algorithms to privately compute mutually interesting meeting points for both drivers and riders in ridesharing. In addition, we have built a prototype implementation of the proposed approach. The experiments, conducted on a real transportation network, have demonstrated that it is possible to reach a trade-off in which both the privacy and utility levels are satisfactory.

Pub.: 15 Oct '16, Pinned: 10 Nov '16

The Ethics of Accident-Algorithms for Self-Driving Cars: an Applied Trolley Problem?

Abstract: Self-driving cars hold out the promise of being safer than manually driven cars. Yet they cannot be a 100 % safe. Collisions are sometimes unavoidable. So self-driving cars need to be programmed for how they should respond to scenarios where collisions are highly likely or unavoidable. The accident-scenarios self-driving cars might face have recently been likened to the key examples and dilemmas associated with the trolley problem. In this article, we critically examine this tempting analogy. We identify three important ways in which the ethics of accident-algorithms for self-driving cars and the philosophy of the trolley problem differ from each other. These concern: (i) the basic decision-making situation faced by those who decide how self-driving cars should be programmed to deal with accidents; (ii) moral and legal responsibility; and (iii) decision-making in the face of risks and uncertainty. In discussing these three areas of disanalogy, we isolate and identify a number of basic issues and complexities that arise within the ethics of the programming of self-driving cars. Self-driving cars hold out the promise of being safer than manually driven cars. Yet they cannot be a 100 % safe. Collisions are sometimes unavoidable. So self-driving cars need to be programmed for how they should respond to scenarios where collisions are highly likely or unavoidable. The accident-scenarios self-driving cars might face have recently been likened to the key examples and dilemmas associated with the trolley problem. In this article, we critically examine this tempting analogy. We identify three important ways in which the ethics of accident-algorithms for self-driving cars and the philosophy of the trolley problem differ from each other. These concern: (i) the basic decision-making situation faced by those who decide how self-driving cars should be programmed to deal with accidents; (ii) moral and legal responsibility; and (iii) decision-making in the face of risks and uncertainty. In discussing these three areas of disanalogy, we isolate and identify a number of basic issues and complexities that arise within the ethics of the programming of self-driving cars.

Pub.: 01 Nov '16, Pinned: 10 Nov '16

Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips

Abstract: Publication date: December 2016 Source:Transportation Research Part A: Policy and Practice, Volume 94 Author(s): Menno D. Yap, Gonçalo Correia, Bart van Arem In the recent years many developments took place regarding automated vehicles (AVs) technology. It is however unknown to which extent the share of the existing transport modes will change as result of AVs introduction as another public transport option. This study is the first where detailed traveller preferences for AVs are explored and compared to existing modes. Its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes, focusing particularly on the use of these vehicles as egress mode of train trips. Because fully-automated vehicles are not yet a reality and they entail a potentially high disruptive way on how we use automobiles today, we apply a stated preference experiment where the role of attitudes in perceiving the utility of AVs is particularly explored in addition to the classical instrumental variables and several socio-economic variables. The estimated discrete choice model shows that first class train travellers on average prefer the use of AVs as egress mode, compared to the use of bicycle or bus/tram/metro as egress. We therefore conclude that AVs as last mile transport between the train station and the final destination have most potential for first class train travellers. Results show that in-vehicle time in AVs is experienced more negatively than in-vehicle time in manually driven cars. This suggests that travellers do not perceive the theoretical advantage of being able to perform other tasks during the trip in an automated vehicle, at least not yet. Results also show that travellers’ attitudes regarding trust and sustainability of AVs are playing an important role in AVs attractiveness, which leads to uncertainty on how people will react when AVs are introduced in practice. We therefore state the importance of paying sufficient attention to these psychological factors, next to classic instrumental attributes like travel time and costs, before and during the implementation process of AVs as a public transport alternative. We recommend the extension of this research to revealed preference studies, thereby using the results of field studies.

Pub.: 19 Sep '16, Pinned: 24 Oct '16

How and why do men and women differ in their willingness to use automated cars? The influence of emotions across different age groups

Abstract: Publication date: December 2016 Source:Transportation Research Part A: Policy and Practice, Volume 94 Author(s): Christoph Hohenberger, Matthias Spörrle, Isabell M. Welpe Current research on willingness to use automated cars indicates differences between men and women, with the latter group showing lower usage intentions. This study aims at providing a first explanation of this effect. Research from other fields suggests that affective reactions might be able to explain behavioral intentions and responses towards technology, and that these affects vary depending on age levels. By examining a sample of 1603 participants representative for Germany (in terms of biological sex, age, and education) we found evidence that affective responses towards automotive cars (i.e., anxiety and pleasure) explain (i.e., mediate) the effect of biological sex on willingness to use them. Moreover, we found that these emotional processes vary as a function of respondent age in such a way that the differential effect of sex on anxiety (but not on pleasure) was more pronounced among relatively young respondents and decreased with participants’ age. Our results suggest that addressing anxiety-related responses towards automated cars (e.g., by providing safety-related information) and accentuating especially the pleasurable effects of automated cars (e.g., via advertising) reduce differences between men and women. Addressing the anxiety-related effects in order to reduce sex differences in usage intentions seems to be less relevant for older target groups, whereas promoting the pleasurable responses is equally important across age groups.

Pub.: 15 Oct '16, Pinned: 24 Oct '16

Why do urban travelers select multimodal travel options: A repertory grid analysis

Abstract: Publication date: November 2016 Source:Transportation Research Part A: Policy and Practice, Volume 93 Author(s): Thomas Clauss, Sebastian Döppe The increasing number of travelers in urban areas has led to new opportunities for local government and private mobility providers to offer new travel modes besides and in addition to traditional ones. Multimodal travel provides an especially promising opportunity. However, until now the underlying reasons why consumers choose specific alternatives have not been fully understood. Hence, the design of new travel modes is mainly driven by obvious criteria such as environmental friendliness and convenience but might not consider consumers’ real or latent needs. To close this research gap, sixty in-depth interviews with urban travelers were conducted. To identify the perceptual differences of customers among different travel modes, the repertory grid technique as an innovative, structured interview method was applied. Our data show that urban travelers distinguish and select travel alternatives based on 28 perceptual determinants. While some determinants associated with private cars such as privacy, flexibility and autonomy are key indicators of travel mode choice, costs and time efficiency also play a major role. Furthermore, by comparing travel modes to an ideal category, we reveal that some perceptual determinants do not need to be maximized in order to fulfill customer needs optimally. A comparison of consumers’ perceptual assessments of alternative travel modes identifies specific advantages and disadvantages of all alternatives, and provides fruitful implications for government and private mobility providers. Graphical abstract

Pub.: 11 Sep '16, Pinned: 24 Oct '16

Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles

Abstract: Publication date: November 2016 Source:Transportation Research Part C: Emerging Technologies, Volume 72 Author(s): Kaidi Yang, S. Ilgin Guler, Monica Menendez Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection. Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology.

Pub.: 30 Sep '16, Pinned: 24 Oct '16