Objectives
The objective of the course is to introduce the students into the legal environment of the autonomous vehicles, including especially the basic principles and guidelines and the present and possible future framework of these laws. - Autonomous vehicles in the recent legal environment, esp. a) public law and private law questions. Autonomous vehicles in the private and public laws, legal frameworks of administrative laws, registrations, torts and product liability, warranty, software-law issues, risk-management, contract-management, insurance issues, b) Data protection (privacy) and data safety issues c) relevant criminal law issues. Autonomous vehicles in the recent legal environment. Criminal issues, and criminal liability - Autonomous vehicles in the Future. a) Types and definitions of autonomous and automated cars. Min-imum requirements, technical compliance standards. b) Future use of autonomous cars and its possible effects on law - use in controlled environments, ride services, etc. c) Human - machine interface and its legal problems; new requirements - e.g. driving licence standards for the human "element" of the system.
Academic results
Knowledge
- the social and economic functions of legislation
- the basic functions of the main areas of law affecting traffic, responsibility, safety
- the main features of the legal, economic and business mechanisms that can influence traffic, responsibility, safety
- relevant approaches to illustrate the impact of regulators on certain questions of autonomous vehicle
- methods and aspects of analysis of legislation affecting autonomous vehicles
Skills
- The students are able to
- properly interpret and place rules in practice
- analyze the role, motivations and activities of individual economic actors from a legal and economic point of view
- grasp a multi-faceted context system for modeling public policy strategy planning in relation to the topic
- critical thinking
Attitude
- are well aware in the assessment of the legal regulation of the autonomous vehicles, is informed by various sources, consciously seeking alternative solutions
- are open to self-reflection, critical reception, and critical thinking when thinking about regulation of auton-omous vehicles
- are open to critical self-assessment, based on activities, active, learning methods, experimental style
Independence and responsibility
- are open to accept reliable critical remarks,
- are able to solve practical professional problems independently.
Teaching methodology
Lectures and written communication, use of ICT tools and techniques.
Materials supporting learning
- Presentations of the lectures.
- Hand-outs dedicated to the actual lecture of the course
General Rules
Assessment of the learning outcomes described under 2.2. is based on two written tests. To complete the course is needed to pass at least 50% each of the midterm exams, to solve the revision questions available on Moodle and attend 70% of the lectures.
Performance assessment methods
Detailed description of assesments during the term: 1. Complex, written assessment of competence-type competence elements in written form. The thesis may con-sist of test questions, which are the interpretation of certain concepts and the recognition of their interrela-tions; essay questions examining lexical knowledge and synthesizing ability. The available working time of 30-90 minutes. 2. In addition, students will have the opportunity to hold a presentation during the semester by applying for the topics provided by the professors. The result of the performance is included in the evaluation.
Percentage of performance assessments, conducted during the study period, within the rating
- Midterm exams: 100
Percentage of exam elements within the rating
Conditions for obtaining a signature, validity of the signature
Assessment of the learning outcomes described under 2.2. is based on two written tests. To complete the course is needed to pass at least 50% each of the midterm exams, to solve the revision questions available on Moodle and attend 70% of the lectures.
Issuing grades
% | |
---|---|
Excellent | 91-100 |
Very good | 85-90 |
Good | 76–84 |
Satisfactory | 63-75 |
Pass | 50-62 |
Fail | < 50 |
Retake and late completion
1) The exams (midterms) will be corrected within the deadline set by the study and examination rules and will be officially published via Neptune. The Department publishes the date of the inspection on a case-by-case basis. 2) It is possible to improve the mark acquired during the year according to the study and examination rules
Coursework required for the completion of the subject
Nature of work | Number of sessions per term |
---|---|
Participation in contact lessons | 28 |
Participationof homework | 10 |
Preparing for the mind-term exam | 52 |
total | 90 |
Approval and validity of subject requirements
Topics covered during the term
Subject includes the topics detailed in the course syllabus to ensure learning outcomes listed under 2.2. can be achi-eved. Timing of the topics may be affected by calendar or other circumstances in each semester.
Lecture topics | |
---|---|
1. | General questions of regulation of autonomous traffic – a social transition by the autonomous vehiclesRegulatory approach to technological development - international and EU regulatory trendsTort lawCriminal liability of AvsAutonomous vehicles – data protection and data safetyMid-term examVehicle and transport legislation in HungaryNew issues of public law regulation, licensing issues, a possible new "KRESZ" .Legal context and possible solutions for the validation of AIInternational best practiceIntellectual property issues relating to autonomous vehicles servicesMobility serviceSummary – analysis of the previous topics, reflectionsMid-term exam |
Additional lecturers
Name | Position | Contact details |
---|---|---|
Dr. Dávid Alíz | vendégelőadó | |
Dr. Mezei Kitti | egyetemi adjunktus | mezei.kitti@gtk.bme.hu |
Dr. Nagy Krisztina | egyetemi adjunktus | nagy.krisztina@gtk.bme.hu |
Dr. Schubauer Petra | egyetemi adjunktus | schubauer.petra@gtk.bme.hu |
Dr. Timár Adrienn | egyetemi tanársegéd | timar.adrienn@gtk.bme.hu |
Dr. Tomasovszky Edit | egyetemi adjunktus | tomasovszky.edit@gtk.bme.hu |