Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

vehicle dynamic model–driver model system: platform to evaluate car and human responses using double lane change circuit

Vehicle Dynamic Model–Driver Model (VDM-DM) system is developed to address the need to have a comprehensive system that can evaluate the performance of the car and the capability of the driver based on the planned trajectory. This is possible when VDM-DM system integrates the vehicle dynamic response with the driver model. The driver model determines the steer input from the geometrical properties of the intended path and this steer angle becomes the input for the vehicle dynamic response analysis. Finally, from the position of the car, the steer angle can be calculated. The position of the car will be then compared with the intended path and a new steer input can be determined by the driver model. Two case studies were carried out to demonstrate the application of the VDM-DM in evaluating the performance of the car and the capability of the driver using Double Lane Change (DLC) circuit. Based on the case studies, VDM-DM can be used as the tool to evaluate the performance of cars and capability of the drivers. This demonstrates that VDM-DM is capable to simulate the behavior of different drivers and hence, VDM-DM system has the potential to bring related road safety issue to the desktop

Personalized driver workload inference by learning from vehicle related measurements

Adapting in-vehicle systems (e.g. Advanced Driver Assistance Systems, In-Vehicle Information Systems) to individual drivers’ workload can enhance safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in a proper manner. Rather than developing an average model for all drivers, a Personalized Driver Workload Inference (PDWI) system considering individual drivers’ driving characteristics is developed using machine learning techniques via easily accessed Vehicle Related Measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers’ workload is first automatically splitted into different categories according to its inherent data characteristics using Fuzzy C means clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating Fuzzy C-means clustering and Support Vector Machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score and prediction time. The inter-individual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness

Assessing Crash Risks on Curves

In Queensland, curve related crashes contributed to 63.44% of fatalities, and 25.17% required hospitalisation. In addition, 51.1% of run-off-road crashes occurred on obscured or open-view road curves (Queensland Transport, 2006). This paper presents a conceptual framework for an in-vehicle system, which assesses crash risk when a driver is manoeuvring on a curve. Our approach consists of using Intelligent Transport Systems (ITS) to collect information about the driving context. The driving context corresponds to information about the environment, driver, and vehicle gathered from sensor technology. Sensors are useful to detect drivers’ high-risk situations such as curves, fogs, drivers’ fatigue or slippery roads. However, sensors can be unreliable, and therefore the information gathered from them can be incomplete or inaccurate. In order to improve the accuracy, a system is built to perform information fusion from past and current driving information. The integrated information is analysed using ubiquitous data mining techniques and the results are later used in a Coupled Hidden Markov Model (CHMM), to learn and classify the information into different risk categories. CHMM is used to predict the probability of crash on curves. Based on the risk assessment, our system provides appropriate intervention to the driver. This approach could allow the driver to have sufficient time to react promptly. Hence, this could potentially promote safe driving and decrease curve related injuries and fatalities

End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners

For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a more complete view of what is happening around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. We investigate the problem in a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such a sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: 1) by representing the planned routes on OpenStreetMap as a stack of GPS coordinates, and 2) by rendering the planned routes on TomTom Go Mobile and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera, in particular for city driving and intersection scenarios; and 2) route planners help the driving task significantly, especially for steering angle prediction.Comment: to be published at ECCV 201

Sähköbussin nopeuden ja ohjauskulman säätö edellä ajavan ajoneuvon liike-radan seuraamisessa

Buses face problems when the capacity of a bus is limited but it should be larger to be able to carry more passengers. The capacity of a bus is already increased to its maximum that is allowed by the infrastructure. The capacity of a bus line could be increased by driving buses more frequently but it would increase costs, that is unwanted. Costs could be reduced by driving buses as platoons consisting of two buses where only the first bus would be operated by a professional driver and the second would be driven autonomously. Autonomous driving requires longitudinal and lateral control of a vehicle. The follower bus should be able to follow the path driven by the leader bus precisely and avoid inter-vehicular collisions while still driving as close together as possible to indicate other traffic that they move as a platoon. Lateral control is usually divided into path following and direct following methods in the literature. Path following methods include obtaining the path of the leader vehicle and following of that path. Path following methods are usually accurate in terms of lateral error but are complex and require a lot of computational capacity. Direct following methods are easy to compute but they do not guarantee precise path following. A simulation model consisting of two identical buses was developed. One longitudinal controller and four lateral control laws were proposed. Longitudinal controller was designed to work also in tight turns which is not usually investigated. Lateral control laws proposed were geometrical in nature and required only input as the relative position of the leader bus. Therefore, they did not require inter-vehicular communication. Longitudinal controller worked well for initialization of the system with inter-vehicular distances from 2 to 10 m. It worked well in acceleration and deceleration tests when both buses were loaded similarly but failed to prevent collisions when follower bus was loaded more heavily than the leader. In lateral controller tests, Pure Pursuit and Modified Pure Pursuit methods were able to follow the leader producing following lateral errors: 0,8 m and 1,1 m (steady-state tests), 0,8 m and 0,7 m (u-turn maneuver) and 0,3 m/0,4 m and 0,4 m/0,5 m (double lane change maneuver, 5 m/s/10 m/s respectively). Spline Pursuit method showed oscillatory behavior and did not follow the leader well. Circular Pursuit method showed also oscillatory behavior and did not follow the leader well. However, it showed better performance than the Spline Pursuit. It remains to be studied whether Pure Pursuit or Modified Pure Pursuit can challenge more sophisticated path following methods.Linja-autojen matkustajakapasiteetti on rajallinen, mikä aiheuttaa ongelmia, sillä sen tulisi olla suurempi. Kapasiteetti on jo nostettu suurimmalle mahdolliselle tasolle, mitä nykyinen infrastruktuuri mahdollistaa. Linja-autolinjan kapasiteettia voisi nostaa ajamalla linja-autoja tiheämmin. Tämä kuitenkin johtaa suurempiin kustannuksiin. Kustannuksia voisi vähentää ajamalla linja-autoja kahden ajoneuvon jonoina, joissa ensimmäistä ajo-neuvoa ohjaisi ammattilaiskuljettaja ja toinen olisi autonomisesti ohjattu. Autonominen ajaminen vaatii ajoneuvon nopeuden ja ohjauskulman säätöä. Seuraajalinja-auton pitää pystyä seuraamaan johtajalinja-auton ajamaa ajouraa tarkasti ja välttää törmäämistä johtajaan. Linja-autojen välinen etäisyys on kuitenkin oltava riittävän pieni, jotta se viestisi muulle liikenteelle, että ajoneuvot ajavat jonona. Kirjallisuus jakaa ohjauskulman säädön yleensä ajouran seuraamiseen ja suoraan seuraamiseen. Ajouran seuraaminen koostuu johtaja-ajoneuvon ajouran saamisesta ja tämän uran seuraamisesta. Ajouran seuraamisen metodit ovat yleensä tarkkoja poikittaisen virheen suhteen, mutta ovat monimutkaisia ja vaativat paljon laskennallista kapasiteettia. Suoran seuraamisen metodit ovat laskennallisesti kevyitä, mutta eivät takaa tarkkaa ajouran seuraamista. Kahdesta identtisestä linja-autosta koostuva simulaatiomalli kehitettiin. Yksi nopeussäädin ja neljä ohjauskulman säätölakia esitettiin. Nopeussäädin suunniteltiin toimimaan myös tiukoissa käännöksissä, mitä ei ole yleensä tutkittu. Ohjauskulman säätölait perustuivat geometriseen päättelyyn ja ne tarvitsivat vain johtajalinja-auton suhteellisen asentotiedon. Säätölait eivät vaatineet ajoneuvojen välistä kommunikaatiota. Nopeussäädin toimi järjestelmän alustamisessa ajoneuvojen välisen etäisyyden ollessa 2-10 m. Se toimi hyvin kiihdytys- ja jarrutustesteissä, kun molemmat linja-autot olivat lastattu identtisellä kuormalla, mutta epäonnistui estämään törmäämisen, kun seuraajalinja-auto oli lastattu suuremmalla kuormalla kuin johtaja. Ohjauskulman säädön testeissä Pure Pursuit ja Modified Pure Pursuit pystyivät seuraamaan johtajaa seuraavilla poikittaissuuntaisilla virheillä: 0,8 m ja 1,1 m (steady-state-testit), 0,8 m ja 0,7 m (u-käännös) ja 0,3 m/0,4 m ja 0,4 m/0,5 m (kaksoiskaistanvaihto, 5 m/s/10 m/s vastaavasti). Spline Pursuit käyttäytyi värähtelevästi eikä seurannut johtajaa hyvin. Circular Pursuit käyttäytyi värähtelevästi eikä seurannut johtajaa hyvin, mutta kuitenkin paremmin kuin Spline Pursuit. Jää nähtäväksi pystyykö Pure Pursuit tai Modified Pure Pursuit haastamaan monimutkaisempia ajouran seuraamisen metodeja