Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development.

/pdf/driving-style-recognition-for-intelligent-vehicle-control-513cyzuxpi.pdf

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis

Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

This thesis is about the safety verification of dynamical systems using reachability analysis. Novel solutions have been developed for classical reachability analysis, stochastic reachability analysis, and their application to the safety assessment of autonomous cars.

Classical reachability analysis computes the set of states that can be reached by a system. If the reachable set does not intersect any set of unsafe states, the safety of the system is guaranteed. Algorithms for this problem have been developed for linear, nonlinear, and hybrid systems.

Stochastic reachability analysis measures the probability of reaching an unsafe set. One pursued approach computes over-approximative solutions for linear systems; another one generates a Markov chain which approximately computes the stochastic reachable set of arbitrary dynamics.

https://mediatum.ub.tum.de/doc/1287517/file.pdf

Reachability Analysis and its Application to the Safety Assessment of Autonomous Cars

Objective:The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety.Background:Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking.Method:A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety.Results:A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and ind...

/pdf/a-review-of-research-on-driving-styles-and-road-safety-5gd4ww77us.pdf

A Review of Research on Driving Styles and Road Safety

| All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.

Driver modeling based on driving behavior and its evaluation in driver identification : Analysis of car-pedal use and car-following habits may allow the help offered by intelligent driver assistance systems to be customized for individual drivers

Spectral analysis is applied to such driving behavioral signals as gas and brake pedal operation signals for extracting drivers' characteristics while accelerating or decelerating. Cepstral features of each driver obtained through spectral analysis of driving signals are modeled with a Gaussian mixture model (GMM). A GMM driver model based on cepstral features is evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle on a city road. Experimental results show that the driver model based on cepstral features achieves a driver identification rate of 89.6% for driving simulator and 76.8% for real vehicle, resulting in 61 % and 55 % error reduction, respectively, over a conventional driver model that uses raw driving signals without spectral analysis

Cepstral Analysis of Driving Behavioral Signals for Driver Identification

In this chapter, driver characteristics under driving conditions are extracted through spectral analysis of driving signals. We assume that characteristics of drivers while accelerating or decelerating can be represented by “cepstral features” obtained through spectral analysis of gas and brake pedal pressure readings. Cepstral features of individual drivers can be modeled with a Gaussian mixture mode! (GMM). Driver models are evaluated in driver identification experiments using driving signals of 276 drivers collected in a real vehicle on city roads. Experimental results show that the driver model based on cepstral features achieves a 76.8 % driver identification rate, resulting in a 55 % error reduction over a conventional driver model that uses raw gas and brake pedal operation signals.

Driver Identification Based on Spectral Analysis of Driving Behavioral Signals

All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following dis- tance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.

Yoshihiro Nishiwaki

Papers

Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

Driver modeling based on driving behavior and its evaluation in driver identification : Analysis of car-pedal use and car-following habits may allow the help offered by intelligent driver assistance systems to be customized for individual drivers

Cepstral Analysis of Driving Behavioral Signals for Driver Identification

Driver Identification Based on Spectral Analysis of Driving Behavioral Signals

Driver Modeling Based on Driving Behavior and Its Evaluation in Driver