A fully autonomous vehicle will require a complete rethink of a vehicle’s electrical architecture is designed and implemented. We discuss what changes will be needed and what new components will be needed.

• Examine the crossovers between ADAS and autonomous systems to see what technologies can be brought forward to the level 5 vehicle

•  Build in a secure architecture through redundancy and fail safe protocols to maintain safe vehicle function in the event of a failure or cyber breach

• Effectively position and combine a suite of sensors e.g. stereoscopic for depth and camera for resolution to have full spectrum awareness of the environment

As sensing technologies continue to increase in resolution, they surpass simple detection and ranging functions and instead take on true “vision” functions in terms of classification and mapping.

• Examine how, through sensor fusion, the combined view of objects from different sensor types enables the vehicle to progress from sensing to “perception”

• Discuss the advances in resolution in automotive sensors (especially radar), review possible fusion processing architectures, broadening your notion of what “vision” will mean for automation

• Understand how the ability of a vehicle to process complex visual data will yield far more accurate detection, classification and localization abilities

With recent hacks and subsequent headlines, it’s no surprise that cybersecurity is at the forefront of the automotive industry. We go beyond the hype to identify what it really means in the active safety industry.

• Why do we need cyber security? Analyse the attack vectors in active safety systems to build a secure vehicle architecture

• Consider the requirements of cyber security in a variety of system layouts e.g. integrated network and centralised processor, to establish what layout offers the most secure architecture

• Focus your cyber security efforts on the priorities of the autonomous vehicle, such as preventing corruption of world models, for secure and safe automation

Trust is arguably the biggest barrier to gaining consumer buy in to automated technology. To overcome this hurdle, a consumer centric approach to system design is needed.

• Go beyond focus groups to understand how advanced data sciences and in vehicle monitoring can be utilised to enhance systems for the consumer

• Look into how you communicate real world test data to prove the efficacy of the technology via effective media channels; alleviating consumer concerns and accelerating adoption

• Manage driver distraction/attentional characteristics by utilizing monocular imaging system data in an intuitive driver feedback HUD

There has been a surge in developments surrounding the autonomous vehicle in recent months. We identify who the major players are in this sector and what is on the horizon for autonomy.

• Map out the landscape. Analyse what the major players, from OEMs to tier 1s to software companies, are focussing on in this space

• After a spate of M&A activity in this sector, get to grips with where the industry stands right now and debate whether such activity is fuelling or stifling innovation

• Analyse the strategies of OEMs, old and new, to understand the route major automakers are taking to autonomy and where your solution can fit in 

The active safety industry is continuing to experience exponential growth. Take a look at the trends that are and will expedite this growth and further shape the industry landscape.

• Take stock of the current value of this market and the new players making inroads into this innovative space

• What’s fuelling the growth in this sector? Examine the next wave of ADAS taking to the market, such as auto lane change, that is set to grow this automotive sector

• Examine the impact of the AEB agreement on ADAS legislation. Has this opened the floodgates for self-regulation?

Automation adds a new level of complexity to vehicle system development. We examine how a process approach can ease development processes.

• Examine how the V model can be utilized to manage requirements and provide traceability in automated system development

• Understand the industry standards, such as Automotive SPICE, you can apply to accelerate and refine your automated system development procedures

• Look into the validation and verification processes that can be applied to HW/SW development ensuring systems meet specified requirements

Advanced Emergency Braking systems will be standard on all new vehicles by 2022. But what are the implications of this landmark agreement on the wider industry?

• The race is on to meet the 2022 deadline! Take a look at where OEMs currently stand in the roll out of AEB to assess market growth potential.

• Analyse the implications of the agreement to the wider industry, such as insurers and legislators, to map out the impact of these systems on the wider automotive ecosystem

• Discuss how the agreement has spurred advances in R&D in the active safety space, facilitating the rise of the next technology to be standardised on all new vehicles

Perceiving the world around it is one of the most critical functions a vehicle will have in the age of ADAS and autonomy.

• Evaluate the attributes of sensor sets to create the perfect sensor suite for reliable and robust environmental mapping

• Combat the challenge of differing highway structures i.e. lane markings pose to systems such as lane departure through a suite of mapping and localized updates

• Look at how the accuracy of object recognition software can be drastically improved through machine learning and categorization algorithms

Real world testing and test tracks provide the foundation for testing ADAS and autonomous systems in real world environments, so what is the progress in these areas.

• Take a global perspective on the advanced testing being undertaken in closed facilities. Who can give you the necessary technological, legal, and infrastructure backup needed?

• Dispel fears over road hour limitations by partnering with or launching mobility projects in order to collect driving data in large quantities

• Examine what parameters test centres are working towards and how they can simulate multiple environments e.g. metropolitan, rural to assess system dynamics in multiple scenarios 

There are still vast hurdles to overcome as we advance towards a world where cars drive themselves. We address what are arguably 2 of the greatest challenges facing engineers today.

• Dissect how best to integrate critical motion planning data, such as path planning and localization data, to optimize vehicle motion

• Go beyond sensor fusion to consider how current classification algorithms need to be optimized through situational learning; alleviating the risks of false positive detection

• Look at how the collaboration between these 2 sides of autonomy can be enhanced, potentially through centralized/fully integrated processors, to bring us closer to L4. 

A vehicles awareness of its position in an environment is essential for automation. We uncover the progress and developments in this field.

• Overlay data onto pre mapped locations to Increase the operational effectiveness of integrated autonomous vehicle maps by overlaying infrastructure and vision system data

• Evaluate the sensor and processor fusion needed to create a simultaneous localiztaion and mapping (SLAM) system

• Grid modelling, HD mapping –weigh up the effectiveness of the variety of mapping and localisation solutions  to focus your efforts on the most promising technology

Existing safety systems such as airbags will soon be fused with active safety systems to form a fully integrated safety system. But what requirements are there for such systems?

•  Enable the pre-deployment of passive safety systems through integration with active safety sensors to dramatically decrease accidents mortality rates

• Examine the level of fusion required for a fully integrated safety system to ascertain their feasibility and what additions will have to made to existing architecture

• Consider how sensor accuracy can be enhanced via robust algorithms to avoid false positive deployment of safety systems

Developments in AI could be the technology that heralds the fully autonomous vehicles need.

• From categorization to situational variation ; consider the requirements of the thinking vehicle to accelerate these developments

• Compare a neural networks capabilities in terms of perception and machine intelligence to that of a processor to establish what technologies will dominate the autonomous race

• Go deeper to examine how deep neural networks, such as AlphaGo, will provide the level of machine learning needed for a completely automated vehicle that can deal with all situations

States are clamouring for involvement in the autonomous race, but how can states and OEMs work together to create a mutually beneficial partnership?

• Outline the positions of states currently involved in autonomous vehicle testing to understand the requirements you need to meet to have testing approved

• Assess the knowledge gaps the state legislators have on this technology to help create a collaborative relationship where innovation will not be stifled by tight legislation

• State driving laws are based on the vehicle having a driver. Consider the massive legislative paradigm shift that needs to take place for driverless vehicles and how it can be eased in to fuel innovation 

ADAS and autonomous tech. is being developed by different organizations on every level of the automotive value chain, but how can components from different manufacturers work in harmony?

• Examine the need for the standardized autonomous system parameters to enable the safe functioning of cross brand systems

• Machine intelligence is on the rise. But what factors do we need to consider for regulation and security?

• Analyze the need for automated vehicles to communicate with infrastructure over a standardized network to allow safe and secure movement of vehicles