2021 ERTRAC网联自动化出行路线草案：征求意见

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《网联、协作与自动化出行路线图（征求意见稿）》是欧洲运输研究联盟(ERTRAC)于2021年9月30日发布的最新版本。这份路线图聚焦于连接性与自动驾驶技术的发展，旨在通过征求公众意见，共同探讨和规划未来交通运输系统的演变方向。该文档作为草案状态分发，目的是为了收集产业专家、研究机构和公共部门的意见和建议，以便在最终确定前进行改进和完善。该路线图涵盖了Connected, Cooperative and Automated Mobility（连通、合作与自动化出行）的关键领域，强调了欧洲在这一领域的领先地位。ERTRAC的工作组“连接性和自动驾驶”在宝马、ASFINAG、沃尔沃集团和博世等业界巨头的领导下，由多位专家组成，如来自ERTRAC办公室的Xavier Aertsens，Stellantis-CRF的Luisa Andreone，ERTICO ITSEurope的Stephane Dreher，以及BASt的Torsten Geissler等人参与了路线图的起草工作。内容涉及的主要知识点包括但不限于： 1. **连通性**：涵盖5G、V2X（Vehicle-to-Everything）通信技术的应用，以及如何增强车辆与基础设施、其他车辆及云端之间的实时信息交换，提升道路安全和交通效率。 2. **协作驾驶**：探讨智能交通系统中车辆之间的协同决策，通过车辆间通信和数据共享来实现避障、优化行驶路径和减少交通拥堵。 3. **自动化水平**：区分不同的自动化等级，从部分自动化到完全自动化，分析每级自动化在实际应用中的挑战与机遇。 4. **技术成熟度评估**：路线图可能包含了对未来关键技术的成熟度评估，如传感器技术、人工智能算法、自动驾驶软件等，以及它们对出行方式变革的影响。 5. **政策与法规**：讨论如何在法律框架内推动自动化出行的发展，包括数据隐私保护、责任归属、测试和认证标准等问题。 6. **标准制定**：强调标准化在促进技术互操作性和市场接受度方面的作用，以及ERTRAC在此过程中的角色。 7. **公众参与与接受度**：考虑如何通过教育和沟通提高公众对连通、协作和自动化出行的理解与接纳度。公众和相关利益方可在指定日期之前向ERTRAC提交他们的反馈和建议，以共同塑造未来的出行发展趋势。这个征求意见稿显示了欧洲在智能交通领域的前瞻思考，反映了其对构建一个高效、安全且可持续的未来交通系统的承诺。

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Regulation

• Safe operation of higher level of automation (L3-L4) in Assisted Corridors

• Harmonized regulation for testing connected vehicle automation in the different European

Member States.

2.2 Confined Areas

Description

Confined areas typically have the surrounding under control with perimeter protection and gates to

prevent un-authorized entry of vehicles and people. In this domain there could be mixed traffic with

manually operated vehicles and other automated vehicles. Vehicles are usually also operated at lower

speeds and there could also be specific traffic regulations. Since confined areas are usually under

supervision there is much lower risk for un-authorized vehicle and VRU presence. Typical examples

of confined areas are “Closed parking areas and parking houses”, “Logistics terminals and port areas”

and “Bus depots and Bus terminals”.

Motivation

Confined areas are well suited for early introduction of highly automated (L4) vehicles due to the

reduced risks for unauthorized traffic and opportunities for productivity improvements. Vehicles in

confined areas usually operates at lower speeds and local traffic regulation might apply which will

reduce ODD complexity considerable. Yet there might be other vehicles and unprotected people

moving around the area so the risk for interference is not negligible.

The confined areas will enable early deployment of CCAM functionality due to lower speed and traffic

complexity. Deployment will be done to improve safety, convenience and productivity for the confined

area operators.

Use Cases

Typical high-level use-cases for operation in the confined areas.

• L4 Car valet parking to improve convenience and parking facility efficiency

• L4 Shuttles in slower speed in restricted areas without safety driver (remote surveillance) for

people and freight.

• L4 Bus self-maneuvering in depot operation to improve safety and productivity

• L4 Unmanned truck/trailer operation in-Terminal/Hub to improve productivity and safety

Societal benefits

There are opportunities to enhance productivity and safety in CCAM operated vehicles confined areas.

But due to the nature of autonomous vehicles users need to further develop trust. Hence there is a need

to increase user and society acceptance of safety of L4 vehicles. Autonomous vehicles provide

considerable opportunities to further enhance productivity and safety in confined areas.

Demonstrations

Demonstration activities should further support deployment to ensure of perceived usefulness and

safety. There is also a need to proceed with European harmonization of confined area use-cases to

further reduce costs and speed up deployment. It is also an opportunity to replicate solutions for

confined areas in Europe and beyond internationally.

Vehicle Enablers

• L4 capable vehicles with parameter setting

• Realtime reaction on connected safety functions

Infrastructure Enablers

Page 12 of 64

• Realtime Traffic control and surveillance in place.

• Possibility to ensure high-bandwidth low-latency connectivity over the domain

• Perimeter safety control using for example gates, fences and geo-fences

• Mechanism to ensure acceptable performance also in non-perfect conditions (e.g.

redundancy)

Validation Enablers

• Functional Safety of the whole traffic system

• Efficient validation toolchain

Standardisation

• Connectivity and C-ITS Interfaces

• Functional Safety of Infrastructure

• Traffic control and surveillance

• Common evaluation of perception performance (vehicles and infra)

• Interfaces to non-confined domain (e.g. gate procedures)

Regulation

• Safety operation in confined areas.

2.3 Urban mixed traffic

Description

In the short and mid-term improving safety and efficiency in metropolitan areas and cities is one of the

most important societal objectives in mobility. Self-driving vehicles bear the potential to crucially

contribute to solve the increasing challenges of urban mixed traffic. One key question is how to

integrate automation into an intermodal mobility system that includes a wide variety of vehicles with

different technical solutions, delivery services and transport of goods, public and private transport, car-

sharing and fleet operations as well as pedestrians, bicyclists and powered two-wheelers.

Therefore, an incremental approach considering the big variety of use cases and linked ODDs appears

being the most promising way to deploy road automation in urban applications. It will be feasible to

introduce automated driving in cities within this decade at least in restricted applications, such as on

dedicated routes where other traffic is limited. But it will be longer before such systems are capable of

completely dealing with all complex urban traffic situations independently.

Motivation

Automated driving technologies being implemented and applied in urban environments will contribute

to the achievement of different societal goals.

Safety: Automated driving is expected to reduce road crashes caused by human errors, thereby

contributing to the so-called Vision Zero, i.e. no road fatalities and serious injuries by 2050.

Efficiency and environment: It will increase the efficiency of the transport system and reduce traffic

congestion. In turn, smoother traffic and less kilometres driven will decrease energy consumption and

emissions.

Accessibility and social inclusion: Automated on-demand mobility services complement the range of

public transport services and provide access to public transport for people who have not yet had access

to public transport. It will be beneficial for people with reduced mobility, providing them with a new

source of mobility and increased access to city centers and remote locations.

Clearly defined low-speed use cases enable early deployment based on available technologies

especially in cities.

Page 13 of 64

Societal benefits

Safety:

In the EU in 2018 38% of all road fatalities occurred in urban area (Key figures 2020 – Road safety

targets, DG MOVE). Considering that urban traffic is typically low speed, the number of non-fatal

crashes with personal injury is disproportionately higher in urban areas compared to non-urban roads.

Safety-enhancing measures in cities will thus be particularly effective in the reduction of injuries.

Efficiency and the environment:

Automated driving technologies will increase the efficiency of the transport system and reduce traffic

congestion regardless of whether they are used in individual transport, in collective mobility services

or in goods transport. In turn, smoother traffic will reduce fuel consumption and emissions. This is

especially true in urban traffic due to the concentrated demand for mobility. Here, too, however, the

prerequisite is a high penetration rate of highly automated vehicles and organized interaction in the

traffic environment. Automated driving technologies, for example improved fleet management, have

the potential to optimize vehicle usage and future mobility services. In the end, this optimised use of

automated mobility modes even shows a potential to reuse space in urban environments and improve

the liveability in cities.

Accessibility and social inclusion:

With automated on-demand mobility services, people with reduced mobility will have a new source of

mobility and better access to city centers and remote locations. Automated driving allows people to

stay mobile for longer and greater participation in their social life. The population in cities will continue

to grow in the coming years – and with it the need for transport. With fully automated and driverless

vehicles this gap can be closed. This will not remove the existing mobility offers (taxis, public

transportation, etc.) in cities, but will complement them in an efficient way.

Demonstrations

Demonstration projects shall support the impact assessment of L4 automation use cases like car valet

parking and shuttle services and facilitate the assessment of acceptance and affordability. Moreover,

demonstration projects will offer room for the early deployment of L4 automated driving in public

transport, goods delivery and municipal services in urban areas.

Use Cases (examples)

The most beneficial applications of automated vehicles in urban area require the ability to drive

autonomously without a driver who must remain alert and ready to take control if the system is unable

to execute the task. One example is Automated Valet Parking, where the real added value comes from

the driver even leaving the vehicle before parking. So the relevant urban use cases are Level 4

applications. Deployment will happen stepwise and according to very defined ODDs (typically low

speed) and infrastructure needs of the specific functionality.

Use cases can be clustered in

• Restricted applications like automated parking, gated areas working with low speed,

• Red carpet use cases on dedicated lanes,

• Residential last mile transport of people and goods,

• Bus(-like) applications on pre-defined routes in mixed traffic and

• Taxi-like operations working on flexible routes in a defined road network.

Vehicle Enablers

Predictive, automated driving requires a situational understanding. The prerequisites for this include,

for example, an understanding of topology, traffic guidance, intentions, road rules, patterns of

behaviour and the interaction between road users.

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